• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.109-112
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• 2016

This paper analyzes the outage performance of a two-way relay network in the presence of interference from multiple interferers. We investigate a two-way relay network where a single user communicates with a selected other user via a relay during three phases. We propose a user selection scheme and analyze an outage probability. Numerical results verify our analysis by comparison with computer simulation and show effects of the number of users and the number of interferers on its the outage probability.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.719-724
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• 2012

A traffic detection system is a device that collects traffic information around an intersection. Most existing traffic detection systems provide very limited traffic information for signal control due to the restriction of vehicle detection area. A signal control scheme determines the transition among signal phases and the time that a phase lasts for. However, the existing signal control scheme do not resolve the traffic congestion effectively since they use restricted traffic information. In this paper, a new traffic detection system with a zone division signal control scheme is proposed to provide correct and detail traffic information and decrease the vehicle's waiting time at the intersection. The traffic detection system obtains traffic information in a way of vehicle-to-roadside communication between vehicles and sensor network. A new signal control scheme is built to exploit the sufficient traffic information provided by the proposed traffic detection system efficiently. Simulation results show that the proposed signal control scheme has 121 % and 56 % lower waiting time and delay time of vehicles at an intersection than other fuzzy signal control scheme.

• The Journal of Engineering Research
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• v.3 no.1
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• pp.85-95
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• 1998

In this study, we consider a recursive procedure to obtain the stationary probability distribution for analyzing Coxian queueing networks with finite queues. This network deals with multiple class customers. Due to the state space representing multiple class customers, the sub-matrices corresponding to states can not be square matrices and can not be inverted. Therefore, we introduce more complex recursive method to avoid the singular problem. The open queueing network that we study consists of 3 parallel first-level sources linked to a single second level queue. We consider two types of schemes for entering a queue. The first scheme is assumed to be the first-blocked-first-enter (FBFE) and the second scheme is the higher-priority-first-enter (HPFE). Arrival and service times are assume to have a Coxian distribution with two phases. Comparison between the resulting using Gauss-Seidel method and recursive procedure will be shown.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.21-30
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• 2004

In Ad-Hoc network, the cluster structure enables effective use of multiple channels, reduces the number of control messages, and increase the scalability of network, Also, it is employed for reducing the number of broadcast messages in an Ad-Hoc network. With the consideration of these advantages, it is desirable that a cluster structure keeps a few clusters in the network, Generally, the cluster formation scheme based on connectivity yields fewer clusters than the other schemes. However, the connectivity based scheme may yield even more clusters than the other schemes according to the network topology. In this paper, a cluster formation scheme dividing the cluster formation into two phases is proposed. In the first phase, the lowest connectivity host in neighborhood initiates the cluster formation. Then, an adjustment procedure for affiliating a lot of the lowest connectivity hosts is employed. In the second phase, the hosts which were not affiliated to the first phase clusters are grouped into one or more clusters through criterions of connectivity and host ID. As a result, the proposed scheme yields a fewer clusters compared with existing other schemes in fully distributed method. The simulation results proves that our scheme is better than LIDCP(3) and HCCP(3).

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.214-221
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• 1998

A new control approach using neural network for the robust position control of a BRUSHLESS direct drive(BLDD) motor is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust BLDD motor system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system will be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained by error back-propagation at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. In addition, the robustness is also obtained without affecting overall system response.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.68-75
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• 2016

As the number of cores and IPs increase in multiprocessor system-on-chip (MPSoC), network-on-chip (NoC) has emerged as a promising novel interconnection architecture for its parallelism and scalability. However, minimization of the latency in NoC with legacy bus IPs must be addressed. In this paper, we focus on the latency minimization problem in NoC which accommodates legacy bus protocol based IPs considering the trade-offs between hop counts and path collisions. To resolve this problem, we propose separated address/data network for independent address and data phases of bus protocol. Compared to Mesh and irregular topologies generated by TopGen, experimental results show that average latency and execution time are reduced by 19.46% and 10.55%, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.26-32
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• 2008

A pedestrian navigation system, an information delivery server, and a program for naturally guiding (such as speech-guiding) the user of a portable terminal at an intersection. An information delivery server comprises a map database containing data such as nodes including paths constituting intersections, links, and costs of the links. The node-link structure is the most important part in pedestrian navigation system. Functional requirements for the road map database vary in different navigation phases. though there are various road network models, their traditional node-link structures, unfortunately, do not solve the problem well. This paper proposes a node-link structure for pedestrian navigation system. The network topological structure in pedestrianl network is presented, which accords with the practical walking habit better than traditional way treating the entire road network.

• Informatization Policy
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• v.18 no.3
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• pp.3-24
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• 2011

The purpose of this paper is to indicate possible future research directions for social network services(SNS) by reviewing past and recent trends in SNS studies. The framework used for the analysis is the New Media Evolutionary Model(NMEM) proposed by Wimmer and Dominick, a four-phase system for research on new media development. Although early forms of SNS emerged in the late 1990s, most research in this field has been published in the past five years. We searched for SNS-related articles published from 2006 to August 2011 from academic journal archives in information systems, communication, marketing, and other fields, and classified them according to the NMEM to analyze the current state of SNS research. Researchers in this field have so far focused on the first two phases of the model(the media itself and use of the media), but little research has been conducted on the third(effects of the media) and fourth phases(improvements in the media). Although SNS research is still in its early stages, we suggest the need for more studies on the effects of SNS and how it can be improved. Very few studies test existing theories or build new theories related to SNS. Thus, a more rigorous approach towards SNS research is warranted, and future research should focus on theory building and testing.