• Journal of Communications and Networks
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• 제17권6호
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• pp.558-567
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• 2015

In recent years, we have seen a proliferation of mobile-network-enabled smart objects, such as smart-phones and smart-watches, that form a cyber-physical integrated network to connect the cyber and physical worlds through the capabilities of sensing, communicating, and computing. Discovery of the relationship between smart objects is a critical and nontrivial task in cyber-physical integrated network applications. Aiming to find the most stable relationship in the heterogeneous and dynamic cyber-physical network, we propose a distributed and efficient relationship-discovery algorithm, called dynamically maximizing remaining unchanged time with minimum connected dominant set (DMRUT-MCDS) for constructing a backbone with the smallest scale infrastructure. In our proposed algorithm, the impact of the duration of the relationship is considered in order to balance the size and sustain time of the infrastructure. The performance of our algorithm is studied through extensive simulations and the results show that DMRUT-MCDS performs well in different distribution networks.

• Journal of the Korean Institute of Intelligent Systems
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• 제17권4호
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• pp.472-476
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• 2007

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.3789-3808
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• 2017

This paper studies a multi-antenna wireless relay network in the presence of a jammer. In this network, the source node transmits signals to the destination node through a multi-antenna relay node which adopts the amplify-and-forward scheme, and the jammer attempts to inject additive signals on all antennas of the relay node. With the linear beamforming scheme at the relay node, this network can be modeled as an equivalent Gaussian arbitrarily varying channel (GAVC). Based on this observation, we deduce the mathematical closed-forms of the capacities for two special cases and the suboptimal achievable rate for the general case, respectively. To reduce complexity, we further propose an optimal structure of the beamforming matrix. In addition, we present a second order cone programming (SOCP)-based algorithm to efficiently compute the optimal beamforming matrix so as to maximize the transmission rate between the source and the destination when the perfect channel state information (CSI) is available. Our numerical simulations show significant improvements of our propose scheme over other baseline ones.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2006년도 춘계종합학술대회
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• pp.91-94
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• 2006

KORDI(Korea Ocean Research & Development Institute) built a research vessel JANG-MOK with 40 G/T for a survey and observation of oceanographic environmental characteristics at coastal region in September 2005. This paper introduced the implementation for hardware and software of an integrated information network that loaded in JANG-MOK, and depicted the function of an integrated information network, such as an installation of ga-bit based network, RS232C serial & UDP network interface of instruments, a data logging software of measured data, Hawkeye II software for supporting the efficient survey works, and a real-time navigation viewer. In addition, we presents the another implementation method for an integrated information network of oceanographic research vessels.

• ETRI Journal
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• 제30권5호
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• pp.696-706
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• 2008

With the increasing need for intelligent environment monitoring applications and the decreasing cost of manufacturing sensor devices, it is likely that a wide variety of sensor networks will be deployed in the near future. In this environment, the way to access heterogeneous sensor networks and the way to integrate various sensor data are very important. This paper proposes the common system for middleware of sensor networks (COSMOS), which provides integrated data processing over multiple heterogeneous sensor networks based on sensor network abstraction called the sensor network common interface. Specifically, this paper introduces the sensor network common interface which defines a standardized communication protocol and message formats used between the COSMOS and sensor networks.

• Journal of Satellite, Information and Communications
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• 제2권2호
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• pp.85-91
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• 2007

The satellite networks provide global coverage. The integration of terrestrial networks with a satellite network is the most attractive approach to develop a global communication system. The IP Multimedia Subsystem (IMS) is intended to be the system that will merge the internet with the telecom world. A user with a dual-mode terminal can access both the satellite network and terrestrial network. The seamless handoff between two networks and a user's QoS level is the major issue concerning this integration. In this paper, we propose IMS-based satellite/terrestrial integrated network architecture for a global communication system. Based on the proposed architecture, an inter-network handoff and end-to-end soft QoS procedure is discussed. Our proposed soft QoS scheme is also analyzed to calculate the number of rejected calls.

• Journal of Institute of Control, Robotics and Systems
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• 제13권5호
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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.

• Journal of the Korean Institute of Intelligent Systems
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• 제16권4호
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• pp.460-465
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• 2006

This paper presents a new fuzzy learning rule which considers the Euclidean distances between the input vector and the prototypes of classes. The new fuzzy learning rule is integrated into the supervised IAFC neural network 4. This neural network is stable and plastic. We used iris data to compare the performance of the supervised IAFC neural network 4 with the performances of back propagation neural network and LVQ algorithm.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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• pp.29-32
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• 2001

This paper describes a development of the integrated controller system for car electrical signal control with CAN communication protocol. The CAN protocol is a robust serial bus system for the control of distributed module in the multiplexed network. After a brief of the main features of the CAN will be addressed, this paper presents the result of the development of the integrated hardware system overall control program.

• Asia pacific journal of information systems
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• 제1권1호
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• pp.63-86
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• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.