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

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4A
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• pp.342-351
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• 2008

In this paper, a new handover procedure for OFDM-based multi-hop relay systems is proposed to reduce the handover overhead by distinguishing inter-cell handover event from intra-cell handover event at the level of physical layer using a hierarchical design concept of preamble. A Subcell ID concept for identifying RS in a cell is proposed in the design of hierarchical manner, in addition to the existing Cell ID for identifying BS. The decision on either inter-cell handover or intra-cell handover is made by the signal quality measure of CBINR(Carrier of BS to Interference and Noise Ratio) and CRINR(Carrier of RS to Interference and Noise Ratio), provided by the hierarchical preamble. The proposed handover procedure can simplify scanning procedure and skip/simplify network re-entry procedure (capability negotiation, authorization, registration), resulting in a significant reduction of handover overhead.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.403-412
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• 2010

Multi-Relay Networks should accommodate mobile users of various speeds. The cellular system should meet the minimum residency time requirements for handover calls while considering an efficient use of available channels. In this paper, we design speed-sensitive handover under dynamic hierarchical cellular systems, in which mobile users are classified according to the mean speed of mobile users and each class has its cellular layer. In order to meet the minimum residency time, the cell size of each cellular layer is dynamically determined depending on the distributions of mean speeds of mobile users. Since the speed-dependent non-preferred cell can provide a secondary resource, overflow and take-back schemes are adopted in the system. We develop analytical models to study the performance of the proposed system, and show that the optimal cell size improves the blocking probability.

• International Journal of Contents
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• v.9 no.4
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• pp.11-15
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• 2013

"Protein Folding Problem" is considered to be one of the "Great Challenges of Computer Science" and prediction of disordered protein is an important part of the protein folding problem. Machine learning models can predict the disordered structure of protein based on its characteristic of "learning from examples". Among many machine learning models, we investigate the possibility of multilayer perceptron (MLP) as the predictor of protein disorder. The investigation includes a single hidden layer MLP, multi hidden layer MLP and the hierarchical structure of MLP. Also, the target node cost function which deals with imbalanced data is used as training criteria of MLPs. Based on the investigation results, we insist that MLP should have deep architectures for performance improvement of protein disorder prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.123-127
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• 2012

Since the propagation of computers and Internet along with proliferation of smartphones rise, a large amount of data is being produced and modified daily. As the usage of data soars, a way of securely storing data emerged as a new problem. In this paper, saving big-data by using hierarchical data structure with multi-layer form, to come up with new security file format and API by applying encryption on each layers, is introduced. Moreover, we expect to see shown file format in this paper to be used in various fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3602-3620
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• 2016

Dynamic topology is one of the main influence factors on network performability. However, it was always ignored by the traditional network performability assessment methods when analyzing large-scale mobile ad hoc networks (MANETs) because of the state explosion problem. In this paper, we address this problem from the perspective of complex network. A two-layer hierarchical modeling approach is proposed for MANETs performability assessment, which can take both the dynamic topology and multi-state nodes into consideration. The lower level is described by Markov reward chains (MRC) to capture the multiple states of the nodes. The upper level is modeled as a small-world network to capture the characteristic path length based on different mobility and propagation models. The hierarchical model can promote the MRC of nodes into a state matrix of the whole network, which can avoid the state explosion in large-scale networks assessment from the perspective of complex network. Through the contrast experiments with OPNET simulation based on specific cases, the method proposed in this paper shows satisfactory performance on accuracy and efficiency.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.790-795
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• 1994

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item, component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introducd to Hierarchical Artificial Neural Network(HANN) for this purpose. HANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification,forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trainined by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing HANN with multitasking and message transfer between processes in SUN workstation. We tested HANN in reactor system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.140-148
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• 1994

In this paepr, the modified neocognitron, which has capability of recognizing multi-patterns in input image in one pass, is proposed. It is the hierarchical neural network composed of S and V layer which are able to extract features and of C layer with little effect from deformation, changes in size, shifts in position. S and V cells extract the features of all patterns in input image by applying DCC(don't care condition) to those cells. S and C cells also have position informations of extracted patterns. Position information is used in extracting good features without extracted features beting interfered one another. The proposed method is shorter in recognition time than the selective attention method with backward connection, because of recognizing multi-patterns in one passe. The modified neocognitron can recognizze attached multi-patterns because of using DCC and position informations.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.35-44
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• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.645-652
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• 2023

The aleurone layer in maize is crucial as it contains essential nutrients such as minerals, vitamins, and high-quality proteins. While most of the maize varieties are known to possess a single aleurone layer, several multi-aleurone layer mutants and landraces have been suggested for hierarchical genetic control of aleurone development. Conventional microscopy analysis often involves using immature seeds or sampling only a portion of the kernel sample, and whole kernel section analysis using a microtome is technically difficult and time-consuming. Additionally, the larger size of maize kernels posed challenges for comprehensive cross-sectional analysis compared to other cereal crops. Consequently, this study aimed to develop an efficient method to comprehensively understand the aleurone layer characteristics of the entire cross-section in maize. Through observations of diverse maize genetic resources, we confirmed irregular aleurone layer patterns in those with multiple aleurone layers, and we discovered a landrace having multiple aleurone layers. By selectively identifying genetic resources with multiple aleurone layers, this method may contribute to efficient breeding processes in maize.