• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4157-4175
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• 2020

Moving target defense, as a 'game-changing' security technique for network warfare, realizes proactive defense by increasing network dynamics, uncertainty and redundancy. How to select the best countermeasure from the candidate countermeasures to maximize defense payoff becomes one of the core issues. In order to improve the dynamic analysis for existing decision-making, a novel approach of selecting the optimal countermeasure using game theory is proposed. Based on the signal game theory, a multi-stage adversary model for dynamic defense is established. Afterwards, the payoffs of candidate attack-defense strategies are quantified from the viewpoint of attack surface transfer. Then the perfect Bayesian equilibrium is calculated. The inference of attacker type is presented through signal reception and recognition. Finally the countermeasure for selecting optimal defense strategy is designed on the tradeoff between defense cost and benefit for dynamic network. A case study of attack-defense confrontation in small-scale LAN shows that the proposed approach is correct and efficient.

• Smart Structures and Systems
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• v.31 no.5
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• pp.501-516
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• 2023

Dynamic irregularity and acceleration of bridges subjected to high-speed trains provide crucial information for comprehensive evaluation of the health state of under-track structures. This paper proposes a novel approach for real-time estimation of vertical track dynamic irregularity and bridge acceleration using deep generative adversarial network (GAN) and vibration data from in-service train. The vehicle-body and bogie acceleration responses are correlated with the two target variables by modeling train-bridge interaction (TBI) through least squares generative adversarial network (LSGAN). To realize supervised learning required in the present task, the conventional LSGAN is modified by implementing new loss function and linear activation function. The proposed approach can offer pointwise and accurate estimates of track dynamic irregularity and bridge acceleration, allowing frequent inspection of high-speed railway (HSR) bridges in an economical way. Thanks to its applicability in scenarios of high noise level and critical resonance condition, the proposed approach has a promising prospect in engineering applications.

• International journal of advanced smart convergence
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• v.2 no.1
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• pp.6-11
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• 2013

Network Coding (NC) is a new paradigm for network communication. In network coding, intermediate nodes create new packets by algebraically combining ingress packets and send it to its neighbor node by broadcast manner. NC has rapidly emerged as a major research area in information theory due to its wide applicability to communication through real networks. Network coding is expected to improve throughput and channel efficiency in the wireless multi-hop network. Many researches have been carried out to employ network coding to wireless ad-hoc network. In this paper, we proposed a dynamic queue management to improve coding opportunistic to enhance efficiency of NC. In our design, intermediate nodes are buffering incoming packets to encode queue. We expect that the proposed algorithm shall improve encoding rate of network coded packet and also reduce end to end latency. From the simulation, the proposed algorithm achieved better performance in terms of coding gain and packet delivery rate than static queue management scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.414-421
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• 2007

This paper presents a engine/brake integrated VDC(Vehicle Dynamic Control) system using neural network algorithm methods for wheel slip and yaw rate control. For stable performance of vehicle, not only is the lateral motion control(wheel slip control) important but the yaw motion control of the vehicle is crucial. The proposed NNPI(Neural Network Proportional-Integral) controller operates at throttle angle to improve the performance of wheel slip. Also, the suggested NNPID controller performs at brake system to improve steering performance. The proposed controller consists of multi-hidden layer neural network structure and PID control strategy for self-learning of gain scheduling. Computer Simulation have been performed to verify the proposed neural network based control scheme of 17 dof vehicle dynamic model which is implemented in MATLAB Simulink.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.133-139
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• 1997

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• Journal of the Korea Society for Simulation
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• v.8 no.4
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• pp.33-51
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• 1999

In this study, the system level network simulation is considered with adaptive array antenna in CDMA mobile communication system. A network simulation framework is implemented based on IS-95A/B system to consider dynamic handoff, system level network behavior, and deploying strategy into the overall CDMA mobile communication network under adaptive array algorithm. Its simulation model, such as vector channel model, adaptive beam forming antenna model, handoff model, and power control model, are described in detail with simulation block. In order to maximize SINR of received signal at antenna, Maximin algorithm is particularly considered, and it is computed at each simulation snap shot with SINR based power control and handoff algorithm. Graphic user interface in this system level network simulator is also implemented to define the simulation environments and to represent simulation results on real mapping system. This paper also shows some features of simulation framework and simulation results.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.423-433
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• 1996

This study was conducted to predict the drafts of various tillage tools with a model tool and a neural network. Drafts of tillage tools were measured and a time lagged recurrent neural network was developed. The neural network had a structure to predict dynamic draft, having a function of one step ahead prediction . The results showed the model tool draft had linear relations with high coefficient of determinations to the drafts of the tillage tools. Also, the drafts of tillage tools were successfully predicted by the developed neural network.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.7-12
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• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• ETRI Journal
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• v.40 no.5
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• pp.604-612
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• 2018

To solve the problem of unbalanced loads and the short network lifetime of heterogeneous wireless sensor networks, this paper proposes a node-selection algorithm based on energy balance and dynamic adjustment. The spacing and energy of the nodes are calculated according to the proximity to the network nodes and the characteristics of the link structure. The direction factor and the energy-adjustment factor are introduced to optimize the node-selection probability in order to realize the dynamic selection of network nodes. On this basis, the target path is selected by the relevance of the nodes, and nodes with insufficient energy values are excluded in real time by the establishment of the node-selection mechanism, which guarantees the normal operation of the network and a balanced energy consumption. Simulation results show that this algorithm can effectively extend the network lifetime, and it has better stability, higher accuracy, and an enhanced data-receiving rate in sufficient time.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.199-205
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• 2022

The extensive growth in data rate demand by the smart gadgets and mobile broadband application services in wireless cellular networks. To achieve higher data rate demand which leads to aggressive frequency reuse to improve network capacity at the price of Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) has been recognized as an effective scheme to get a higher data rate and mitigate ICI for perfect geometry network scenarios. In, an irregular geometric multicellular network, ICI mitigation is a challenging issue. The purpose of this paper is to develop distributed dynamic power allocation scheme for FFR based on game theory to mitigate ICI. In the proposed scheme, each cell region in an irregular multicellular scenario adopts a self-less behavior instead of selfish behavior to improve the overall utility function. This proposed scheme improves the overall data rate and mitigates ICI.