• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.47 no.5
• /
• pp.9-17
• /
• 2010

In this paper we propose an improved hierarchical routing protocol for wireless hybrid mesh network. The proposed method efficiently manages network topology and reduces overhead traffic for setting routing path by considering link stability. The simulation results show that the proposed method outperforms the HOLSR (hierarchical optimized link state routing) method in aggregate goodput, packet delivery ratio, and end-to-end delay.

• Proceedings of the ESK Conference
• /
• 1996.10a
• /
• pp.197-205
• /
• 1996

Unification of neural network with a hierarchical pattern recognition is presented for recognizing large set of objects. A two-step identification procedure is developed for pattern recognition: coarse and fine identification. The coarse identification is designed for finding a class of object while the fine identification procedure is to identify a specific object. During the training phase a course neural network is trained for clustering larger set of reference objects into a number of groups. For training a fine neural network, expert neural network is also trained to identify a specific object within a group. The presented idea can be interpreted as two step identification. Experimental results are given to verify the proposed methodology.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.8
• /
• pp.889-897
• /
• 2014

With the development of wireless communication and sensor technology, sensor network applications in various fields have been applied. To minimize the power consumption of sensors in sensor network is one of the important factors in oder to extend the system life. The power consumption of each sensor within sensor network can be different depending on the communication method between head(sink) node and its node. In this paper, we propose a new hierarchical communication method to minimize the power consumption of each sensor. The proposed method divides the area of sensor network into four areas using divide-and-conquer method and selects the nearest node to head node in each area as a child node of the node. Next the hierarchical tree in the same way is constructed recursively until each area is no longer divided. Each sensor can communicate to head node using this hierarchical tree. The proposed results were compared with the previous methods through simulation, and showed excellent results in the energy efficiency of sensor network.

• Journal of Sensor Science and Technology
• /
• v.26 no.1
• /
• pp.1-6
• /
• 2017

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.12
• /
• pp.4293-4304
• /
• 2014

In order to ensure the wireless connectivity and seamless service to mobile users, the next generation network system will be an integration of multiple wireless access networks. In a heterogeneous wireless access system, bandwidth allocation becomes crucial for load balancing to avoid network congestion and improve system utilization efficiency. In this article, we propose a new dynamic bandwidth allocation scheme for hierarchical wireless network systems. First, we derive a multi-objective decision criterion for each access point. Second, a bargaining strategy selection algorithm is developed for the dynamic bandwidth re-allocation. Based on the intervenient Stackelberg game model, the proposed scheme effectively formulates the competitive interaction situation between several access points. The system performance of proposed scheme is evaluated by using extensive simulations. With a simulation study, it is confirmed that the proposed scheme can achieve better performance than other existing schemes under widely diverse network environments.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.1
• /
• pp.76-82
• /
• 2002

In this paper, we implemented on the classifier for differential diagnosis of laryngeals disease which is normal, polyp, nodule, palsy, and each step of glottic cancer using hierarchical neural network. We conducted on classifier of various vowels as /a/, /e/, /i/, /o/, /u/ from normal group, laryngeal disease group, each step of cancer group. The experimental result on classification of each vowels as follows. A /a/ vowel shows excellent classification result to the other vowels in regard to each Input parameters. Thus we implemented the hierarchical neural network for differential diagnosis of laryngeals disease using only /a/ vowel. A implemented hierarchical neural network is composed of each other laryngeals disease apply to each other parameter in each hierarchical layer. We take the voice signals from patient who get the laryngeal disease and glottic cancer, and then use the APQ, PPQ, vAm, Jitter, Shimmer, RAP as input parameter of neural networks.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.3
• /
• pp.1319-1327
• /
• 2015

As wind power farm (WPF) installations continue to grow, monitoring and controlling large-scale WPFs presents new challenges. In this paper, a hierarchical network architecture is proposed in order to provide remote monitoring and control of large-scale WPFs. The network architecture consists of three levels, including the WPF comprised of wind turbines and meteorological towers, local control center (LCC) responsible for remote monitoring and control of wind turbines, and a central control center (CCC) that offers data collection and aggregation of many WPFs. Different scenarios are considered in order to evaluate the performance of the WPF communications network with its hierarchical architecture. The communications network within the WPF is regarded as the local area network (LAN) while the communication among the LCCs and the CCC happens through a wide area network (WAN). We develop a communications network model based on an OPNET modeler, and the network performance is evaluated with respect to the link bandwidth and the end-to-end delay measured for various applications. As a result, this work contributes to the design of communications networks for large-scale WPFs.

• The KIPS Transactions:PartA
• /
• v.9A no.3
• /
• pp.333-340
• /
• 2002

Embedding is a mapping an interconnection network G to another interconnection network H. If a network G can be embedded to another network H, algorithms developed on G can be simulated on H. In this paper, we first propose a method to embed between Hierarchical Cubic Network HCN(n, n) and Hierarchical Folded-hypercube Network HFN(n, n). HCN(n, n) and HFN(n, n) are graph topologies having desirable properties of hypercube while improving the network cost, defined as degree${\times}$diameter, of Hypercube. We prove that HCN(n, n) can be embedded into HFN(n, n) with dilation 3 and congestion 2, and the average dilation is less than 2. HFN(n, n) can be embedded into HCN(n, n) with dilation 0 (n), but the average dilation is less than 2. Finally, we analyze the fault tolerance of HCN(n, n) and prove that HCN(n, n) is maximally fault tolerant.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.1
• /
• pp.88-98
• /
• 1994

A new neural network architecture called the Parallel. Self-Organizing Hierarchical Neural Network (PSHNN) is presented. The new architecture involves a number of stages in which each stage can be a particular neural network (SNN). The experiments performed in comparison to multi-layered network with backpropagation training and indicated the superiority of the new architecture in the sense of classification accuracy, training time,parallelism.

• The KIPS Transactions:PartC
• /
• v.11C no.7 s.96
• /
• pp.905-916
• /
• 2004

Self-organizing hierarchical ad hoc network (SOHAN) is a new ad-hoc network architecture designed to improve the scalability properties of conventional 'flat' ad hoc networks. This network architecture consists of three tiers of ad-hoc nodes, i.e.. access points, forwarding nodes and mobile nodes. This paper presents a topology discovery and routing protocol for the self-organization of SOHAN. We propose a cross-layer path metric based on link quality and MAC delay which plays a key role in producing an optimal cluster-based hierarchical topology with high throughput capacity. The topology discovery protocol provides the basis for routing which takes place in layer 2.5 using MAC addresses. The routing protocol is based on AODV with appropriate modifications to take advantage of the hierarchical topology and interact with the discovery protocol. Simulation results are presented which show the improved performance as well as scalability properties of SOHAN in terms of through-put capacity, end-to-end delay, packet delivery ratio and control overhead.