• International journal of advanced smart convergence
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• v.13 no.2
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• pp.16-24
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• 2024

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.2
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• pp.74-82
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning control fuzzy neural network (AFLC-FNN) controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC-FNN controller. Also, this paper proposes speed control of IPMSM using AFLC-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.83-97
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• 2015

IEEE 802.15.4 is a global standard designed for emerging applications in low-rate wireless personal area networks (LR-WPANs). The standard provides beneficial features, such as a beacon-enabled mode and guaranteed time slots for realtime data delivery. However, how to optimally operate those features is still an open issue. For the optimal operation of the features, this paper proposes a holistic optimization method that jointly optimizes three cross-related problems: cluster-tree construction, nodes' power configuration, and duty-cycle scheduling. Our holistic optimization method provides a solution for those problems so that all the real-time packets can be delivered within their deadlines in the most energy-efficient way. Our simulation study shows that compared to existing methods, our holistic optimization can guarantee the on-time delivery of all real-time packets while significantly saving energy, consequently, significantly increasing the lifetime of the network. Furthermore, we show that our holistic optimization can be extended to take advantage of the spatial reuse of a radio frequency resource among long distance nodes and, hence, significantly increase the entire network capacity.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.527-537
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• 2013

Improving resource utilization has been a hot issue in heterogeneous wireless networks (HWNs). This paper proposes a radio resource management (RRM) method based on access proportion optimization. By considering two or more wireless networks in overlapping regions, users in these regions must select one of the networks to access when they engage in calls. Hence, the proportion of service arrival rate that accesses each network in the overlapping region can be treated as an optimized factor for the performance analysis of HWNs. Moreover, this study considers user mobility as an important factor that affects the performance of HWNs, and it is reflected by the handoff rate. The objective of this study is to maximize the total throughput of HWNs by choosing the most appropriate factors. The total throughput of HWNs can be derived on the basis of a Markov model, which is determined by the handoff rate analysis and distribution of service arrival rate in each network. The objective problem can actually be expressed as an optimization problem. Considering the convexity of the objective function, the optimization problem can be solved using the subgradient approach. Finally, an RRM optimization scheme for HWNs is proposed. The simulation results show that the proposed scheme can effectively enhance the throughput of HWNs, i.e., improve the radio resource utilization.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.239-247
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• 2008

In a resource-limited wireless communication environment, various approaches to meet the ever growing application requirements in an efficient and transparent manner, are being researched and developed. Amongst many approaches, cross layer technique is by far one of the significant contributions that has undoubtedly revolutionized the way conventional layered architecture is perceived. In this paper, we propose a Universal Cross Layer Framework based on vertical layer architecture. The primary contribution of this paper is the functional architecture of the vertical layer which is primarily responsible for cross layer interaction management and optimization. The second contribution is the use of optimization cycle that comprises awareness parameters collection, mapping, classification and the analysis phases. The third contribution of the paper is the decomposition of the parameters into local and global network perspective for opportunistic optimization. Finally, we have shown through simulations how parameters' variations can represent local and global views of the network and how we can set local and global thresholds to perform opportunistic optimization.

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.299-304
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• 2017

A wireless passive sensor network is a network consisting of sink nodes, sensor nodes, and radio frequency (RF) sources, where an RF source transfers energy to sensor nodes by radiating RF waves, and a sensor node transmits data by consuming the received energy. Against theoretical expectations, a wireless passive sensor network suffers from many practical difficulties: scarcity of energy, non-simultaneity of energy reception and data transmission, and inefficiency in allocating time resources. Perceiving such difficulties, we propose a simple contending-type medium access control (MAC) scheme for many sensor nodes to deliver packets to a sink node. Then, we derive an approximate expression for the network-wide throughput attained by the proposed MAC scheme. Also, we present an approximate expression for the optimal partition, which maximizes the saturated network-wide throughput. Numerical examples confirm that each of the approximate expressions yields a highly precise value for network-wide throughput and finds an exactly optimal partition.

• Journal of KIISE:Information Networking
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• v.32 no.1
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• pp.29-33
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• 2005

It is not future story that host mobility in wireless network. We already have many devices which can access network among moving our locations. Internet protocol is extended to support not only host mobility but also network mobility. But nesting mobile networks and mobile hosts, there is a problem that routing path of packet becomes complex. By sequential tunneling on outgoing packet at mobile router and toward mobile network at correspondent node, it can be setting up direct path in nested mobile network environment. In this paper, we propose a method for delivering packet through optimized path by sequential tunneling. And evaluate this method by equations and simulations.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.