• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.3
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• pp.117-126
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• 2013

This paper is written for the development and application of boiler furnace pressure control algorithm and distributed control system of coal-fired power plant by the steps of design, coding, simulation test, site installation and site commissioning test. The control algorithms were designed in the shape of cascade control for two parts of furnace pressure control and induced draft fan pitch blade by standard function blocks. This control algorithms were coded to the control programs of distributed control systems. The simulator for coal-fired power plant was used in the test step and automatic control, sequence control and emergency stop tests were performed successfully like the tests of the actual power plant. The reliability was obtained enough to be installed at the actual power plant and all of distributed control systems had been installed at power plant and all signals were connected mutually. Tests for reliability and safety of plant operation were completed successfully and power plant is being operated commercially. It is expected that the project result will contribute to the safe operation of domestic new and retrofit power plants, the self-reliance of coal-fired power plant control technique and overseas business for power plant.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.3
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• pp.322-331
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• 2013

This paper describes the development of the pilot plant of distributed intelligent management system for a microgrid. For optimal control and management of microgrids, intelligent agents area applied to the microgrid management system. Each agent includes intelligent algorithms to make decisions on behalf of the corresponding microgrid entity such as distributed generators, local loads, and so on. To this end, each agent has its own resources to evaluate the system conditions by collecting local information and also communicating with other agents. This paper presents key features of the data communication and management of the developed pilot plant such as the construction of mesh network using local wireless communication techniques, the autonomous agent coordination schemes using plug-and-play functions of agents and contract net protocol (CNP) for decision-making. The performance of the pilot plant and developed algorithms are verified via real-time microgrid test bench based on hardware-in-the-loop simulation systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2299-2318
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• 2019

Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.407-417
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• 2003

Enen though several GA-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, GA-based optimization methods are computationally too expensive for practical use in the field of structural optimization, particularly for large- scale problems. Furthermore, a successful implementation of GA-based optimization algorithm requires a cumbersome and trial-and-error routine related to setting of parameters dependent on a optimization problem. Therefore, to overcome these disadvantages, a high-performance GA is developed in the form of distributed hybrid genetic algorithm for structural optimization on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consist of a simple GA running on a master computer and multiple μ-GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computational time required for structural optimization process can be drastically reduced and the dependency on the parameters can be avoided.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.10
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• pp.865-875
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• 2012

In this paper, we optimize antenna locations for a distributed antenna system (DAS) with distributed antenna (DA) ports equipped with multiple antennas under per-DA port power constraint. Maximum ratio transmission and scaled zero-forcing beamforming are employed for single-user and multi-user DAS, respectively. Instead of maximizing the cell average ergodic sum rate, we focus on a lower bound of the expected signal-to-noise ratio (SNR) for the single-cell scenario and the expected signal-to-leakage ratio (SLR) for the two-cell scenario to determine antenna locations. For the single-cell case, optimization of the SNR criterion generates a closed form solution in comparison to conventional iterative algorithms. Also, a gradient ascent algorithm is proposed to solve the SLR criterion for the two-cell scenario. Simulation results show that DAS with antenna locations obtained from the proposed algorithms achieve capacity gains over traditional centralized antenna systems.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.436-446
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• 2014

The high intensity of research and modeling in fields of mathematics, physics, biology and chemistry requires new computing resources. For the big computational complexity of such tasks computing time is large and costly. The most efficient way to increase efficiency is to adopt parallel principles. Purpose of this paper is to present the issue of parallel computing with emphasis on the analysis of parallel systems, the impact of communication delays on their efficiency and on overall execution time. Paper focuses is on finite algorithms for solving systems of linear equations, namely the matrix manipulation (Gauss elimination method, GEM). Algorithms are designed for architectures with shared memory (open multiprocessing, openMP), distributed-memory (message passing interface, MPI) and for their combination (MPI + openMP). The properties of the algorithms were analytically determined and they were experimentally verified. The conclusions are drawn for theory and practice.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.277-283
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• 2019

In this paper, we propose a distributed fusion estimation for sensor networks using a receding horizon strategy. Communication channels were modelled as Markov jump systems, and a posterior probability distribution for communication channel characteristics was calculated and incorporated into the filter to allow distributed fusion estimation to handle path loss observation situations automatically. To implement distributed fusion estimation, a Kalman-Consensus filter was then used to obtain the average consensus, based on the estimates of sensors randomly distributed across sensor networks. The advantages of the proposed algorithms were then verified using a large-scale sensor network example.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.207-211
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• 2023

Traditional methods for datamining typically assume that the data is small, centralized, memory resident and static. But this assumption is no longer acceptable, because datasets are growing very fast hence becoming huge from time to time. There is fast growing need to manage data with efficient mining algorithms. In such a scenario it is inevitable to carry out data mining in a distributed environment and Frequent Itemset Mining (FIM) is no exception. Thus, the need of an efficient incremental mining algorithm arises. We propose the Distributed Incremental Approximate Frequent Itemset Mining (DIAFIM) which is an incremental FIM algorithm and works on the distributed parallel MapReduce environment. The key contribution of this research is devising an incremental mining algorithm that works on the distributed parallel MapReduce environment.

• Journal of KIISE:Databases
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• v.37 no.5
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• pp.228-237
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• 2010

Although conventional index structures provide various nearest-neighbor search algorithms for high-dimensional data, there are additional requirements to increase search performances as well as to support index scalability for large scale data. To support these requirements, we propose a distributed high-dimensional indexing structure based on cluster systems, called a Distributed Vector Approximation-tree (DVA-tree), which is a two-level structure consisting of a hybrid spill-tree and VA-files. We also describe the algorithms used for constructing the DVA-tree over multiple machines and performing distributed k-nearest neighbors (NN) searches. To evaluate the performance of the DVA-tree, we conduct an experimental study using both real and synthetic datasets. The results show that our proposed method contributes to significant performance advantages over existing index structures on difference kinds of datasets.