• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.193-214
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• 2017

In this paper, we study the problem of network utility maximization in a CSMA based multi-hop wireless network. Existing work in this aspect typically adopted continuous time Markov model for performance modelling, which fails to consider the channel conflict impact in actual CSMA networks. To maximize the utility of a CSMA based wireless network with channel conflict, in this paper, we first model its weighted network capacity (i.e., network capacity weighted by link queue length) and then propose a distributed link scheduling algorithm, called CSMA based Maximal-Weight Scheduling (C-MWS), to maximize the weighted network capacity. We derive the upper and lower bounds of network utility based on C-MWS. The derived bounds can help us to tune the C-MWS parameters for C-MWS to work in a distributed wireless network. Simulation results show that the joint optimization based on C-MWS can achieve near-optimal network utility when appropriate algorithm parameters are chosen and also show that the derived utility upper bound is very tight.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.169-178
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• 2005

This paper presented high-level function model to achieve traffic engineering to construct traffic engineering infrastructure in Internet. Function model presented include traffic management, capacity management, and network planing. It is ensured that network performance is maximized under all conditions including load shifts and failures by traffic management. It is ensured that the network is designed and provisioned to meet performance objectives for network demands at minimum cost by capacity management. Also it is ensured that node and transport capacity is planned and deployed in advance of forecasted traffic growth by network planning.

• International Journal of Concrete Structures and Materials
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• v.1 no.1
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• pp.63-73
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• 2007

Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.221-228
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• 2005

Data storage was thought to be inside of or next to server cases but advances in networking technology make the storage system to be located far away from the main computer. In Internet era with explosive data increases, balanced development of storage and transmission systems is required. SAN(Storage Area Network) and NAS(Network Attached Storage) reflect these requirements. It is important to know the capacity and limit of the complex storage network system to got the optimal performance from it. The capacity data is used for performance tuning and making purchasing decision of storage. This paper suggests an analytic model of storage network system as queuing network and proves the model though simulation model.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.352-361
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• 2004

In this paper, we study how to achieve the maximum capacity under delay constraints for large mobile wireless networks. We develop a systematic methodology for studying this problem in the asymptotic region when the number of nodes n in the network is large. We first identify a number of key parameters for a large class of scheduling schemes, and investigate the inherent tradeoffs among the capacity, the delay, and these scheduling parameters. Based on these inherent tradeoffs, we are able to compute the upper bound on the maximum per-node capacity of a large mobile wireless network under given delay constraints. Further, in the process of proving the upper bound, we are able to identify the optimal values of the key scheduling parameters. Knowing these optimal values, we can then develop scheduling schemes that achieve the upper bound up to some logarithmic factor, which suggests that our upper bound is fairly tight. We have applied this methodology to both the i.i.d. mobility model and the random way-point mobility model. In both cases, our methodology allows us to develop new scheduling schemes that can achieve larger capacity than previous proposals under the same delay constraints. In particular, for the i.i.d. mobility model, our scheme can achieve (n-1/3/log3/2 n) per-node capacity with constant delay. This demonstrates that, under the i.i.d. mobility model, mobility increases the capacity even with constant delays. Our methodology can also be extended to incorporate additional scheduling constraints.

• Journal of Communications and Networks
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• v.10 no.4
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• pp.371-383
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• 2008

Application of network coding in wireless two-way relay channels (TWRC) has received much attention recently because its ability to improve throughput significantly. In traditional designs, network coding operates at upper layers above (including) the link layer and it requires the input packets to be correctly decoded. However, this requirement may limit the performance and application of network coding due to the unavoidable fading and noise in wireless networks. In this paper, we propose a new wireless network coding scheme for TWRC, which is referred to as soft network coding (SoftNC), where the relay nodes applies symbol-by-symbol soft decisions on the received signals from the two end nodes to come up with the network coded information to be forwarded. We do not assume further channel coding on top of SoftNC at the relay node (channel coding is assumed at the end nodes). According to measures of the soft information adopted, two kinds of SoftNC are proposed: amplify-and-forward SoftNC (AF-SoftNC) and soft-bit-forward SoftNC (SBF-SoftNC). We analyze the both the ergodic capacity and the outage capacity of the two SoftNC schemes. Specifically, analytical form approximations of the ergodic capacity and the outage capacity of the two schemes are given and validated. Numerical simulation shows that our SoftNC schemes can outperform the traditional network coding based two-way relay protocol, where channel decoding and re-encoding are used at the relay node. Notable is the fact that performance improvement is achieved using only simple symbol-level operations at the relay node.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2840-2853
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• 2015

A major challenge in network service providers is to provide adequate resources in service level agreements based on forecasts of future demands. In this paper, we address the problem of capacity provisioning in a network subject to demand uncertainty such that a network coded multicast is applied as the data delivery mechanism with limited budget to purchase extra capacity. We address some particular type of uncertainty sets that obtain a tractable constrained capacity provisioning problem. For this reason, we first formulate a mathematical model for the problem under uncertain demand. Then, a robust optimization model is proposed for the problem to optimize the worst-case system performance. The robustness and effectiveness of the developed model are demonstrated by numerical results. The robust solution achieves more than 10% reduction and is better than the deterministic solution in the worst case.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.366-381
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• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.

• Health Policy and Management
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• v.17 no.4
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• pp.54-81
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• 2007

The paradigm of health promotion requests community participation and its active problem-solving. Community is conceptualized as a resource pool to be organized. Such resource is called community capacity. Community participation is a process of capacity building. Community voluntary associations are considered as valuable resource to be used for health promotion. This paper tried to identify the network structure among community voluntary associations and to infer the possibility to make such network of organizations participate in health promotion programs. Two survey data were used for this research: 1) Measurements and Evaluations of Community Capacity on Dobong-gu (N=94) 2) A development plan of health medicine service to be Healthy Gangdong-gu (N=69). The questionnaire included such variables measuring community capacity as leadership, membership, organizational resources, and inter-organizational network, etc. Both regions had the following common characteristics: 1) There were positive correlations between the organization's budget and membership. 2) Organizational types were associated with their founded years. Two regions showed the following differences: Dobong displayed the high density of community organizations, but Gangdong showed the low density. Dobong community organizations were able to be classified into three network clusters such as women & environments, youth & adolescent, and sports organizations. Each cluster of organizations favored the different type of health promotion programs. Gangdong community organizations were less developed, and not possible to be clustered. Depending upon the level of community capacity or community organizations' differentiation, the strategy of community participation could be settle down in different ways. Particularly the health agency had to pay more attention to support the growth of civil organizations.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.