• Journal of Information Processing Systems
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• v.19 no.4
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

• Smart Media Journal
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• v.13 no.6
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.7-15
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• 2024

6G network technology represents the next generation of communications, supporting high-speed connectivity, ultra-low latency, and integration with cutting-edge technologies, such as the Internet of Things (IoT), virtual reality, and autonomous vehicles. These advancements promise to drive transformative changes in digital society. However, as technology progresses, the demand for efficient data transmission and energy management between smart devices and network equipment also intensifies. A significant challenge within 6G networks is the optimization of interactions between satellites and smart devices. This study addresses this issue by introducing a new game theory-based technique aimed at minimizing system-wide energy consumption and latency. The proposed technique reduces the processing load on smart devices and optimizes the offloading decision ratio to effectively utilize the resources of Low-Earth Orbit (LEO) satellites. Simulation results demonstrate that the proposed technique achieves a 30% reduction in energy consumption and a 40% improvement in latency compared to existing methods, thereby significantly enhancing performance.

• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• The Journal of the Korea Contents Association
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• v.6 no.7
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• pp.69-78
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• 2006

In this paper, we presents the efficiency and a transmission quality of the system which is composed of the optical elements from physical layer of the active optical ring network. For a simulation it will use the Transmissionmaker WDM and it will be able to observation a optical transmission quality of the optical transmission system. The active optical network is composed of two rings(main ring and sub-ring). It measures the BER(Bit Error Rate) quality which it follows node number from the sub ring and physical distance of the node. Performance analysis from the physical layer becomes the standard of the plan for the efficiency optimization of the active optical ring network. Consequently it will be able to compose the efficient optical transmission system which reflects the physical distance, a traffic demand quantity of each node and a number of users from actual network.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.968-972
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• 2009

The multi-core processors are being widely exploited by many high-end systems. With significant advances in processor architecture, the network band-width required on the high-end systems is increasing drastically. It is therefore highly desirable to manage multiple cores efficiently to achieve high network band-width with minimum resource requirements. Modern operating systems, however, still have significant design and optimization space to leverage the network performance over multi-core systems. In this paper, we suggest a novel networking process scheduling scheme, which decides the best processor affinity of networking processes based on the processor cache layout, communication intensiveness, and processor loads. The experimental results show that the scheduling scheme implemented in the Linux kernel can improve the network bandwidth and the effectiveness of processor utilization by 20% and 59%, respectively.

• Journal of Wetlands Research
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• v.19 no.1
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• pp.103-111
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• 2017

In this study, we estimate the NNI(Nearest Neighbor Index) which is considered altitude of rain gauge network as a method for evaluating appropriateness of spatial distribution and the current rain gauge network is evaluated. The altitude is divided by equal-area-ratio and optimal NNI within given basin condition is estimated using harmony search method for considering geographical conditions that vary from altitude to altitude. After calculating current state and optimal NNI for each altitude, the distribution of the rain gauge network is evaluated based on the difference between the two NNIs. As a result, it founds that the density of rain gauge networks is relatively thin as the altitude increases. Furthermore, it will be possible to construct an efficient rain gauge network if the characteristics of different altitudes are considered when a new rain gauge network is newly constructed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1250-1256
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• 2018

Recently, wireless networks are becoming an important infrastructure for the critical large-scale applications such as cyber-physical systems and next generation industrial automations. However, the fundamental performance uncertainty of wireless networks may incur the serious instability problem of the overall systems. This paper proposes the proactive network optimizer to guarantee the application demands without any real-time link monitoring information of the networks. In particularly, the proposed proactive optimizer is the cross-layer approach to jointly optimize the routing path and traffic distribution in order to guarantee the performance demand within a maximum k number of link faults. Through the simulations, the proposed proactive network optimizer provides better robustness than the traditional existing reactive networks. Furthermore, the proactive network does not expose to the major weakness of the reactive networks such as the performance degradation due to the erroneous link monitoring information and the network reconfiguration cost.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.963-973
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• 2005

Increasing attention is being recently devoted to various problems inherent in the topological design of networks systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cable. Lately, these network systems are well designed with tiber optic cable, because the requirements from users become increased. But considering the high cost of the fiber optic cable, it is more desirable that the network architecture is composed of a spanning tree. In this paper, we present a GA (Genetic Algorithm) for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable, considering the connection cost, average message delay, and the network reliability We also employ the $Pr\ddot{u}fer$ number (PN) and cluster string in order to represent chromosomes. Finally, we get some experiments in order to certify that the proposed GA is the more effective and efficient method in terms of the computation time as well as the Pareto optimality.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.184-197
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• 2015

This paper builds on a recent method, chain routing with even energy consumption (CREEC), for designing a wireless sensor network with chain topology and for scheduling the communication to ensure even average energy consumption in the network. In here a new suboptimal design is proposed and compared with the CREEC design. The chain topology in CREEC is reconfigured after each group of n converge-casts with the goal of making the energy consumption along the new paths between the nodes in the chain as even as possible. The new method described in this paper designs a single near-optimal Hamiltonian circuit, used to obtain multiple chains having only the terminal nodes different at different converge-casts. The advantage of the new scheme is that for the whole life of the network most of the communication takes place between same pairs of nodes, therefore keeping topology reconfigurations at a minimum. The optimal scheduling of the communication between the network and base station in order to maximize network lifetime, given the chosen minimum length circuit, becomes a simple linear programming problem which needs to be solved only once, at the initialization stage. The maximum lifetime obtained when using any combination of chains is shown to be upper bounded by the solution of a suitable linear programming problem. The upper bounds show that the proposed method provides near-optimal solutions for several wireless sensor network parameter sets.