• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.98-103
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• 2021

In this study, we propose a group communication technique that guarantees security and expandability by configuring a network consisting of IoT terminals equipped with security functions. As the number of devices participating in the network increases, network resources are proportionally reduced, and adding a security function to the IoT device increases the delay time due to encryption in the IoT device. If the error rate that occurs in the network increases, network resources are quickly consumed due to retransmission. Therefore, IoT terminals are grouped to ensure scalability while supporting security, reducing the consumption of network resources even when the number of participating nodes increases, thus ensuring scalability. For the future implementation, the encryption method used in IoT terminals considered the standard of IEEE802.5.4, and the standardization trend was investigated and classified. The proposed method applies IoT devices that provide security functions of the IEEE802.5.4 standard to the group communication base to ensure reliability and scalability. In the performance evaluation, the effectiveness of the proposed method was confirmed by comparing the delay times when grouping IoT devices with security functions through simulation.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.371-380
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• 2022

Convolutional neural networks are widely used to manipulate data arranged in a grid, such as images. A general convolutional neural network consists of a convolutional layers and a fully connected layers, and each layer contains a nonlinear activation functions. This paper proposes a combined parametric activation function to improve the performance of convolutional neural networks. The combined parametric activation function is created by adding the parametric activation functions to which parameters that convert the scale and location of the activation function are applied. Various nonlinear intervals can be created according to parameters that convert multiple scales and locations, and parameters can be learned in the direction of minimizing the loss function calculated by the given input data. As a result of testing the performance of the convolutional neural network using the combined parametric activation function on the MNIST, Fashion MNIST, CIFAR10 and CIFAR100 classification problems, it was confirmed that it had better performance than other activation functions.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.573-578
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• 2008

Recently, we should control various devices such as TV, audio, DVD player, video player, and set-top box simultaneously to manipulate home theater system. To execute the function the user want in this situation, user should know functions and positions of the buttons in several remote controllers. Normally, people feel difficult due to these realistic problems. Besides, the number of the devices that we can control shall increase, and people will confuse more if the ubiquitous home environment is realized. Therefore, user adaptive interface that provides the summarized functions is required. Moreover there can be a lot of mobile and stationary controller devices in ubiquitous computing environment, so user interface should be adaptive in selecting the functions that user wants and in adjusting the features of UI to fit in specific controller. To implement the user and controller adaptive interface, we modeled the ubiquitous home environment and used modeled context and device information. We have used Bayesian network to get the degree of necessity in each situation. Behavior selection network uses predicted user situation and the degree of necessity, and it selects necessary functions in current situation. Selected functions are used to construct adaptive interface for each controller using presentation template. For experiments, we have implemented ubiquitous home environment and generated controller usage log in this environment. We have confirmed the BN predicted user requirements effectively as evaluating the inferred results of controller necessity based on generated scenario. Finally, comparing the adaptive home UI with the fixed one to 14 subjects, we confirmed that the generated adaptive UI was more useful for general tasks than fixed UI.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.89-100
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• 2006

The introduction of GSMP open interface to OBS network can materialize to separate the transport plane and the control plane in OBS network. This makes the implementation of OBS switches simple and provides various flexibility. However, the introduction of open interface will cause the connection setup delay because of the additional processing overhead of open interface protocol. Also, in GSMP based network, the location of network management functions are not defined explicitly and the research result about the OBS network management is almost nothing. This paper proposes a parallel connection setup mechanism using centralized connection control server to minimize connection setup delay in OBS network with GSMP open interface and defines managed objects to support connection, configuration, performance, and fault management for the management of OBS network with GSMP open interface. This paper also proposes a distributed network management model, in which the above managed objects are distributed in a controller and an OBS switch according to network management functions. We verify the possibility of OBS control and network management by implementing network management function using proposed parallel connection setup mechanism and distributed network management model.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.867-875
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• 2009

A wireless sensor network inherently comprises a plurality of sensor nodes widely deployed for sensing environmental information. To add new functions or to correct some faulty functions of an existing wireless sensor network, the firmware for each sensor node needs to be updated. Firmware update would be quite troublesome if it requires the gathering, reprogramming, and redeploy of all of already deployed sensor nodes. Over-the-air programming (OTA) facilitates the firmware update process, thereby allowing convenient maintenance of an already-deployed sensor network. This paper proposes and implements a remote firmware update mechanism for a TDMA-based wireless sensor network, in which the firmware for sensor nodes constituting the TDMA-based sensor network can be easily updated and the update process can be conveniently monitored from a remote site. We verify the validity of the proposed firmware update method via experiments and introduce three wireless sensor networks installed in outdoor sites in which the proposed firmware update mechanism has been exploited.

• Convergence Security Journal
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• v.17 no.3
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• pp.3-9
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• 2017

NFV is a technology that allows network services to be controlled and managed in software by separating various net work functions (NFs) from hardware devices in dedicated network equipment and implementing them in a high-performance general-purpose server. Therefore, standardized virtualization of network functions is one of the most important factors. However, until the introduction of NFV to provide commercial services, there are many technical issues to be solved such as guaranteeing performance, stability, support for multi-vendor environment, ensuring perfect interoperability, and linking existing virtual and non-virtual resources. In this paper, we propose a method to provide an end-to-end network virtualization service based on OSM R2 in KREONET.

• ETRI Journal
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• v.42 no.5
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.101-106
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• 2006

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.

• Structural Engineering and Mechanics
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• v.13 no.2
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• pp.187-209
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• 2002

The infrastructure system in the United States has been aging faster than the resource available to restore them. Therefore decision for allocating the resources is based in part on the condition of the structural system. This paper proposes to use neural network to predict the overall rating of the structural system because of the successful applications of neural network to other fields which require a "symptom-diagnostic" type relationship. The goal of this paper is to illustrate the potential of using neural network in civil engineering applications and, particularly, in bridge evaluations. Data collected by the Tennessee Department of Transportation were used as "test bed" for the study. Multi-layer feed forward networks were developed using the Levenberg-Marquardt training algorithm. All the neural networks consisted of at least one hidden layer of neurons. Hyperbolic tangent transfer functions were used in the first hidden layer and log-sigmoid transfer functions were used in the subsequent hidden and output layers. The best performing neural network consisted of three hidden layers. This network contained three neurons in the first hidden layer, two neurons in the second hidden layer and one neuron in the third hidden layer. The neural network performed well based on a target error of 10%. The results of this study indicate that the potential for using neural networks for the evaluation of infrastructure systems is very good.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.