• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.587-594
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• 2012

IEEE 802.15.6 standard technology is proposed for low-power wireless communication in, on and around body, where vital signs such as pulse, blood pressure, ECG, and EEG signals are transmitted as a type of data packet. Especially, these vital signs should be delivered in real time, so that the latency from slave node to hub node can be one of the pivotal performance requirements. However, in the case of IEEE 802.15.6 technology data retransmission caused by transmission failure can be done in the next superframe. In order to overcome this limitation, we propose an adaptive polling algorithm for IEEE 802.15.6 technology. The proposing algorithm makes the hub to look for an appropriate time period in order to make data retransmission within the superframe. Through the performance evaluation, the proposing algorithm achieves a 61% and a 73% latency reduction compared to those of IEEE 802.15.6 technology in the environment of 70% traffic offered load with 10ms and 100ms superframe period. In addition, the proposing algorithm prevents bursty traffic transmission condition caused by mixing retransmission traffic with the traffic reserved for transmission. Through the proposing adaptive polling algorithm, it will be possible to transmit time-sensitive vital signs without severe traffic delay.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.94-104
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• 2020

Currently media consumption over fixed/mobile Internet is mostly conducted by adaptive media streaming technology such as DASH (Dynamic Adaptive Streaming over HTTP), which is an ISO/IEC MPEG (Moving Picture Experts Group) standard, or some other technologies similar to DASH. All these heavily depend on the HTTP caches that ISPs (Internet Service Providers) are obliged to provide sufficiently to make sure fast enough Web services. As a result, as the number of media streaming users increases, ISPs' burden for HTTP cache has been greatly increased rather than CDN (Content Delivery Network) providers' server burden. Hence ISPs charge traffic cost to CDN providers to compensate for the increased cost of HTTP caches. Recently in order to reduce the traffic cost of CDN providers, P2P (Peer-to-Peer)-assisted DASH system was proposed and a peer selection algorithm that maximally reduces CDN provides' traffic cost was investigated for this system. This algorithm, however, tends to concentrate the burden upon the selected peer. This paper proposes a new peer selection algorithm that distributes the burden among multiple peers while maintaining the proper reduction level of the CDN providers' cost. Through implementation of the new algorithm in a Web-based media streaming system using WebRTC (Web Real-Time Communication) standard APIs, it demonstrates its effectiveness with experimental results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.128-135
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• 2021

Thin-walled tubes have been widely used as energy absorbing devices because they are light and have high energy-absorption efficiency. However, the downside is that conventional thin-walled tubes usually exhibit an excessive initial peak crushing force (IPCF) and a large fluctuation in the load-displacement curve, and thus lack stability as energy absorbing devices. Corrugated tubes were introduced to reduce IPCF and to increase the stability of collision energy-absorbing devices. Since the performance of corrugated tubes is highly influence by geometry, design optimization methods can be utilized to optimize the performance of corrugated tubes. In this paper, we utilize shape design optimization based on an adaptive surrogate model for crashworthiness analysis. The amplitude and wavelength of the corrugation, as well as curvature changes in the features, are the design variables. A morphing methodology is adopted to perform shape design parameterization. Through numerical examples, we compare optimal design results based on the adaptive surrogate model, with optimal results based on conventional surrogate models, and we show that direct optimal design methods produce more efficient results.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.49-56
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• 2024

An automated dynamic structural analysis module stands as a crucial element within a structural integrated mitigation system. This module must deliver prompt real-time responses to enable timely actions, such as evacuation or warnings, in response to the severity posed by the structural system. The finite element method, a widely adopted approximate structural analysis approach globally, owes its popularity in part to its user-friendly nature. However, the computational efficiency and accuracy of results depend on the user-provided finite element mesh, with the number of elements and their quality playing pivotal roles. This paper introduces a computationally efficient adaptive mesh generation scheme that optimally combines the h-method of node movement and the r-method of element division for mesh refinement. Adaptive mesh generation schemes automatically create finite element meshes, and in this case, representative strain values for a given mesh are employed for error estimates. When applied to dynamic problems analyzed in the time domain, meshes need to be modified at each time step, considering a few hundred or thousand steps. The algorithm's specifics are demonstrated through a standard cantilever beam example subjected to a concentrated load at the free end. Additionally, a portal frame example showcases the generation of various robust meshes. These examples illustrate the adaptive algorithm's capability to produce robust meshes, ensuring reasonable accuracy and efficient computing time. Moreover, the study highlights the potential for the scheme's effective application in complex structural dynamic problems, such as those subjected to seismic or erratic wind loads. It also emphasizes its suitability for general nonlinear analysis problems, establishing the versatility and reliability of the proposed adaptive mesh generation scheme.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.307-316
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• 2004

Wireless local area networks have known an increasing popularity during the past few years. However, as new user applications diverge from the traditional data-centric model, the introduction of efficient, QoS aware medium access methods becomes of utmost importance. EY-NPMA is a medium access protocol belonging to the contention paradigm that provides support for service differentiation and low collision rates. In this paper, we address a shortcoming of EY-NPMA as indicated by previous studies, namely the insensitivity of the protocol to different working conditions. In this work, we study and evaluate a mechanism that allows a network employing EY-NPMA to adapt its operating parameters according to the offered load. Simulation studies further document and confirm the positive characteristics of the proposed mechanism.

• ETRI Journal
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• v.42 no.4
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• pp.585-595
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• 2020

Integration of multiple communication technologies in a smart grid (SG) enables employing cognitive radio (CR) technology for improving reliability and security with low latency by adaptively and effectively allocating spectral resources. The versatile features of the CR enable the smart meter to select either the unlicensed or the licensed band for transmitting data to the utility company, thus reducing communication outage. Demand response management is regarded as the control unit of the SG that balances the load by regulating the real-time price that benefits both the utility company and consumers. In this study, joint allocation of the transmission power to the smart meter and consumer's demand is formulated as a two stage multi-armed bandit game in which the players select their optimal strategies noncooperatively without having any prior information about the media. Furthermore, based on historical rewards of the player, a real-time pricing adaptation method is proposed. The latter is validated through numerical results.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.1
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• pp.1211-1217
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• 2001

This paper presnts a controller for the heat capacity of thermal storage systems using off-peak electricity which is composed of an identifier using neural networks and a storage time adjuster in order to store exactly the required thermal energy without loss. Since thermal storage systems have nonlinear characteristics and large time constant, even if we predict the heating load accurately, it is very difficult to store exactly the required thermal energy. Thus, in the neural network for the identifier, the adaptive learning rate for high learning speed and bit inputs based on state changes of thermal storage power source are used. Also a hardware for the controller using a microprocessor is developed. The performance of the proposed controller is shown by experiment.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1727-1731
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• 2004

We propose an ART2 neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, fault classifier by ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters is used for fault isolation. The performances of the proposed fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.11
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• pp.70-77
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• 1992

Generally, the conventional tracking algorithms are very limited in the practical applications because of that the computation load is exponentially increased as the number of targets being tracked is increase. Recently, to overcome this kind of limitation, some new tracking methods based on neural network algorithms which have learning and parallel processing capabilities are introduced. By application of neural networks to multi-target tracking problems, the tracking system can be made computationally independent of the number of objects being tracked, through their characteristics of massive parallelism and dense interconnectivity. In this paper, a new neural network tracking algorithm, which has capability of adaptive target tracking with little increase of the amount of calculation under the clutter and noisy environments, is suggested and the possibility of real-time multi-target tracking system based on neural networks is also demonstrated through some good computer simulation results.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.115-118
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• 2004

An abrupt increase of traffic-load in cellular CDMA systems can cause overload and degrade the quality of service (QoS) due to the insufficient radio resources. In this paper, we propose a joint coverage and resource management (JCRM) scheme which can improve the QoS degradation and spectrum utilization. The JCRM scheme hands over overloaded traffic to the neighboring cells by virtually reducing a heavily loaded cell coverage and allocates radio resources based on the necessary handover probability. The proposed scheme can be applied to the existing cellular CDMA systems as well as adaptive coverage management schemes for next generation mobile communication systems.