• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.255-260
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• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.38-43
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• 2008

In this paper, adaptive channel borrowing approach fuzzy neural networks for load balancing (ACB-FNN) is presented to maximized the number of served calls and the depending on asymmetries traffic load problem. In a wireless network, the call's arrival rate, the call duration and the communication overhead between the base station and the mobile switch center are vague and uncertain. A new load balancing algorithm with cell involved negotiation is also presented in this paper. The ACB-FNN exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding better performance compared with other algorithms. It aims to efficiently satisfy their diverse quality-of-service (QoS) requirements. The results show that our algorithm has lower blocking rate, lower dropping rate, less update overhead, and shorter channel acquisition delay than previous methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.3
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• pp.287-294
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• 2000

The present paper describes an investigation into the application of the artificial neural network in the establishment criterion of redevelopment assignment. From the results, it is found that the artificial neural network algolithm search technique is very effective to find the good optimum solution as well as has higher robustness. So, the results of this research could contribute to making a reasonable assessment criterion of redevelopment assignment which is proper to the situation of Korea.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.593-600
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• 1999

This paper suggests a suboptimal control scheme of an active suspension system with an asymmetric hydraulic cylinder. In this paper a quarter car model including a nonlinear actuator dynamics is used. A feedback linearization technique is applied to obtain a linear model. An LQ regulator is designed with the linear model to keep robustness against sprung mass variation. The gain of the LQ regulator which depends on the damping coefficient of the damper is calculated by using an RBF neural network for real time application. The improvement achieved with our design is illustrated through comparative simulations.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.399-410
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• 2021

Aiming at the problem that complex external variables in natural scenes have a greater impact on facial expression recognition results, a facial expression recognition method based on two-stream convolutional neural network is proposed. The model introduces exponentially enhanced shared input weights before each level of convolution input, and uses soft attention mechanism modules on the space-time features of the combination of static and dynamic streams. This enables the network to autonomously find areas that are more relevant to the expression category and pay more attention to these areas. Through these means, the information of irrelevant interference areas is suppressed. In order to solve the problem of poor local robustness caused by lighting and expression changes, this paper also performs lighting preprocessing with the lighting preprocessing chain algorithm to eliminate most of the lighting effects. Experimental results on AFEW6.0 and Multi-PIE datasets show that the recognition rates of this method are 95.05% and 61.40%, respectively, which are better than other comparison methods.

• Journal of Korean Association for Spatial Structures
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• v.21 no.4
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• pp.39-48
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• 2021

In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.376-388
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• 2022

The expansion of new applications and business models is being significantly fueled by the development of Fifth Generation (5G) networks, which are becoming more widely accessible. The creation of the newest intelligent vehicular networks and applications is made possible by the use of Vehicular Ad hoc Networks (VANETs) and Software Defined Networking (SDN). Researchers have been concentrating on the integration of SDN and VANET in recent years, and they have examined a variety of issues connected to the architecture, the advantages of software-defined VANET services, and the new features that can be added to them. However, the overall architecture's security and robustness are still in doubt and have received little attention. Furthermore, new security threats and vulnerabilities are brought about by the deployment and integration of novel entities and a number of architectural components. In this study, we comprehensively examine the good and negative effects of the most recent SDN-enabled vehicular network topologies, focusing on security and privacy. We examine various security flaws and attacks based on the existing SDVN architecture. Finally, a thorough discussion of the unresolved concerns and potential future study directions is provided.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.231-243
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• 2022

The expansion of new applications and business models is being significantly fueled by the development of Fifth Generation (5G) networks, which are becoming more widely accessible. The creation of the newest intelligent vehicular net- works and applications is made possible by the use of Vehicular Ad hoc Networks (VANETs) and Software Defined Networking (SDN). Researchers have been concentrating on the integration of SDN and VANET in recent years, and they have examined a variety of issues connected to the architecture, the advantages of software defined VANET services, and the new features that can be added to them. However, the overall architecture's security and robustness are still in doubt and have received little attention. Furthermore, new security threats and vulnerabilities are brought about by the deployment and integration of novel entities and several architectural components. In this study, we comprehensively examine the good and negative effects of the most recent SDN-enabled vehicular network topologies, focusing on security and privacy. We examine various security flaws and attacks based on the existing SDVN architecture. Finally, a thorough discussion of the unresolved concerns and potential future study directions is provided.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1167-1169
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• 2009

Multipath routing in wireless sensor networks has been proven to provide with increased data delivery ratio, security, robustness to node and link failures, network throughput, etc. However, the energy cost for multiple routes construction and their maintenance is very high. This paper proposes a sink-initiated, node-disjoint multipath routing protocol for static wireless sensor networks that significantly minimizes the route construction messages and thereby saves the critical batter energy of sensor nodes. It also distributes the traffic load spatially over many nodes in the forwarding paths, which ensures balanced energy consumption in the network and thereby increases the network lifetime. The simulation results show that it decreases the routing overhead as well as the standard deviation of nodes' residual energies.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.202-210
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• 2005

This paper presents a new neural network control for nonlinear bridge systems with earthquake excitation. We design multi-layer neural network controllers with a single hidden layer. The selection of an optimal number of neurons in the hidden layer is an important design step for control performance. To select an optimal number of hidden neurons, we progressively add one hidden neuron and observe the change in a performance measure given by the weighted sum of the system error and the control force. The number of hidden neurons which minimizes the performance measure is selected for implementation. A neural network was trained for mitigating vibrations of bridge systems caused by El Centro earthquake. We applied the proposed control approach to a single-degree-of-freedom (SDOF) and a two-degree-of-freedom (TDOF) bridge system. We assessed the robustness of the control system using randomly generated earthquake excitations which were not used in training the neural network. Our results show that the neural network controller drastically mitigates the effect of the disturbance.