• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.465-468
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• 2007

An intelligent home network system using low-power and low-cost sensor nodes was designed and implemented. In Intelligent Home Network System, active home appliances control is composed of RSSI (Received Signal Strength Indicator) based user indoor location tracking, dynamic multi-hop routing, and learning integration remote-control. Through the remote-control learning, home appliances can be controlled in wireless network environment. User location information for intelligent service is calculated using RSSI based Triangle measurement method, and then the received location information is passed to Smoothing Algorithm to reduce error rate. In order to service Intelligent Home Network, moreover, the sensor node is designed to be held by user. The gathered user data is transmitted through dynamic multi-hop routing to server, and real-time user location & environment information are displayed on monitoring program.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.429-432
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• 2003

In this paper, a novel neural network approach is proposed for cleaning robot to complete coverage path planning with obstacle avoidance in stationary and dynamic environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location without any prior knowledge of the dynamic environment.

• Journal of Internet of Things and Convergence
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• v.10 no.5
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• pp.87-93
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• 2024

This paper focuses on the potential field-based routing scheme developed for wireless multihop networks, emphasizing the exploration of the previously proposed virtual node concept to address network hole problems in dynamic network environments. Previous studies have demonstrated the superior performance of integrating the virtual node scheme into this routing strategy under network hole scenarios, but they have not addressed dynamic network conditions. This study directly considers dynamic network changes such as link failures or node breakdowns, validating the adaptability of the potential-field routing scheme integrated with the virtual node concept. Performance evaluations using the NS-3 simulator are conducted to demonstrate its improved adaptability to dynamic network change situations, compared to conventional approaches. This study highlights the significance of exploring new applications of the virtual node concept for effectively managing dynamic network changes in real-world scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.151-155
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• 1996

To automate an excavator the control issues resulting from environmental uncertainties must be solved. In particular the interactions between the excavation tool and the excavation environment are dynamic, unstructured and complex. In addition, operating modes of an excavator depend on working conditions, which makes it difficult to derive the exact mathematical model of excavator. Even after the exact mathematical model is established, it is difficult to design of a controller because the system equations are highly nonlinear and the state variable are coupled. The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbance and performance improvement with the on-line learning in the position control of excavator attachment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1213-1237
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• 2019

Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.139-148
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• 2008

The sensor network is applied from the field which is various. The sensor network nodes are exchanged with mobile environment and they construct they select cluster and cluster headers. In this paper, we propose the Dynamic Prediction Clustering Algorithm use to Skyline queries attributes in direction, angel and hop. This algorithm constructs cluster in base mobile sensor node after select cluster header. Propose algorithm is based made cluster header for mobile sensor node. It "Adv" reduced the waste of energy which mobile sensor node is unnecessary. Respects clustering where is efficient according to hop count of sensor node made dynamic cluster. To extend a network life time of 2.4 times to decrease average energy consuming of sensor node. Also maintains dynamic cluster to optimize the within hop count cluster, the average energy specific consumption of node decreased 14%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.2
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• pp.49-55
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• 2008

To address the convergence issue of power control algorithms, a number of algorithms have been developed hat shape the dynamics of up-link power control for cellular network. Power algorithms based on fixed point iterations can be accelerated by the use of various methods, one of the simplest being the use of Newton iterations, however, this method has the disadvantage which not only needs derivatives of the cost function but also may be weak to noisy environment. we showed performance of the power control schemes to solve the fixed point problem under static or stationary channel. They proved goof performance to solve the fixed point problem due to their predictor based optimal control and quadratic convergence rate. Here, we apply the proposed power control schemes to the problem of the dynamic channel or to dynamic time varying link gains. The rigorous simulation results demonstrated the validity of our approach.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.43-52
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• 2013

Pervasive computing and social network are good resources in recommendation method. Collaborative filtering is one of the most popular recommendation methods, but it has some limitations such as rating sparsity. Moreover, it does not consider social network in pervasive computing environment. We propose an effective proactive friend recommendation method using social network and contexts in pervasive computing environment. In collaborative filtering method, users need to rate sufficient number of items. However, many users don't rate items sufficiently, because the rating information must be manually input into system. We solve the rating sparsity problem in the collaboration filtering method by using contexts. Our method considers both a static and a dynamic friendship using contexts and social network. It makes more effective recommendation. This paper describes a new friend recommendation method and then presents a music friend scenario. Our work will help e-commerce recommendation system using collaborative filtering and friend recommendation applications in social network services.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1320-1330
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• 2017

Wireless sensor networks for forest monitoring are typically deployed in fields in which manual intervention cannot be easily accessed. An interesting approach to extending the lifetime of sensor nodes is the use of energy harvested from the environment. Design constraints are application-dependent and based on the monitored environment in which the energy harvesting takes place. To reduce energy consumption, we designed a power management scheme that combines dynamic duty cycle scheduling at the network layer to plan node duty time. The dynamic duty cycle scheduling is realized based on a tier structure in which the network is concentrically organized around the sink node. In addition, the multi-paths preserved in the tier structure can be used to deliver residual packets when a path failure occurs. Experimental results show that the proposed method has a better performance.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.1-13
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• 2020

Recently, network infrastructure has evolved into a BizTech agile autonomous network to cope with the dynamic changes in the service environment. This survey presents the expectations from two different perspectives of the harmonization of network and artificial intelligence (AI) technologies. First, the paper focuses on the possibilities of AI technology for the autonomous network industry. Subsequently, it discusses how networks can play a role in the evolution of distributed AI technologies.