• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1121-1232
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• 1990

A hierarchical neural network structure is described for robot PTP trajectory planning. In the first level, the multi-layered Perceptron neural network is used for the inverse kinematics with the back-propagation learning procedure. In the second level, a saccade generation model based joint trajectory planning model in proposed and analyzed with several features. Various simulations are performed to investigate the characteristics of the proposed neural networks.

• Proceedings of the Zoological Society Korea Conference
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• 1996.10a
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• pp.108.1-108
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• 1996

No Abstract, See Full Text

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2520-2528
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• 2009

In this paper, we proposed tracking detection methodology using information granulation-based fuzzy radial basis function neural networks (IG-FRBFNN). According to IEC 60112, tracking device is manufactured and utilized for experiment. We consider 12 features that can be used to decide whether tracking phenomenon happened or not. These features are considered by signal processing methods such as filtering, Fast Fourier Transform(FFT) and Wavelet. Such some effective features are used as the inputs of the IG-FRBFNN, the tracking phenomenon is confirmed by using the IG-FRBFNN. The learning of the premise and the consequent part of rules in the IG-FRBFNN is carried out by Fuzzy C-Means (FCM) clustering algorithm and weighted least squares method (WLSE), respectively. Also, Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA) is exploited to optimize the IG-FRBFNN. Effective features to be selected and the number of fuzzy rules, the order of polynomial of fuzzy rules, the fuzzification coefficient used in FCM are optimized by the HFC-PGA. Tracking inference engine is implemented by using the LabVIEW and loaded into embedded system. We show the superb performance and feasibility of the tracking detection system through some experiments.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1881-1892
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• 1997

This paper proposes the method for recognition of printed chinese characters by probabilistic VQ networks and multi-stage recognizer has hierarchical structure. We use modular neural networks, because it is difficult to construct a large-scale neural network. Problems in this procedure are replaced by probabilistic neural network model. And, Confused Characters which have significant ratio of miss-classification are reclassified using the entropy theory. The experimental object consists of 4,619 chinese characters within the KSC5601 code except the same shape but different code. We have 99.33% recognition rate to the training data, and 92.83% to the test data. And, the recognition speed of system is 4-5 characters per second. Then, these results demonstrate the usefulness of our work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3034-3055
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• 2014

Wireless Sensor Networks have extensively been utilized for ambient data collection from simple linear structures to dense tiered deployments. Issues related to optimal resource allocation still persist for simplistic deployments including linear and hierarchical networks. In this work, we investigate the case of dimensioning parameters for linear and tiered wireless sensor network deployments with notion of providing extended lifetime and reliable data delivery over extensive infrastructures. We provide a single consolidated reference for selection of intrinsic sensor network parameters like number of required nodes for deployment over specified area, network operational lifetime, data aggregation requirements, energy dissipation concerns and communication channel related signal reliability. The dimensioning parameters have been analyzed in a pipeline monitoring scenario using ZigBee communication platform and subsequently referred with analytical models to ensure the dimensioning process is reflected in real world deployment with minimum resource consumption and best network connectivity. Concerns over data aggregation and routing delay minimization have been discussed with possible solutions. Finally, we propose a node placement strategy based on a dynamic programming model for achieving reliable received signals and consistent application in structural health monitoring with multi hop and long distance connectivity.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.120-128
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• 2023

In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.49-58
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• 2009

Four alternative plans for the bus network in Daejeon metropolitan city, which have different hierarchical structure, were proposed : Alternative 1 represents a bus network without hierarchical structure, and Alternative 2, 3, and 4 represent bus networks with primary, intermediate, and advanced hierarchical structures, respectively. Efficiency of the alternative plans were evaluated based on the evaluation index including travel time cost, waiting time cost, and transition penalty cost. The travel time cost was decreased as the level of hierarchical structure gets higher until it reaches the extremely high level. As the level of hierarchical structure get higher, the waiting time cost significantly decreased while the transition penalty cost increased. Collectively, a bus network with hierarchical structure was shown to be more efficient than without it in the light of total travel cost. For the bus network with hierarchical structure, total travel cost shows a concave curve, which implies that there exists an optimal level of hierarchical structure in a bus network.

• Journal of KIISE:Information Networking
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• v.32 no.5
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• pp.603-614
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• 2005

The wireless LAN environment using Mobile IP is constructed and managed to be connected with Ethernet based wired networks. However, there have been many problems with wireless networks using Mobile IP. Some important facts on network performance have not been considered when introducing wireless LAN by Mobile IP to wired networks. In this paper, we suggest schemes which can solve problems on Handover latency caused by the asymmetrical connectivity of the Access Router at applying the HMIPv6 and on binding updates due to the MN frequent movement. Our proposed schemes can reduce network latency by using the HMIPv6 architecture with a virtual router layer, and reduce communication overhead by interchanging information of the MN movement between routers. Our schemes are expected to assist in constructing a more real and effective wireless LAN environment based on the HMIPv6 and FMIP.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.8
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• pp.23-32
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• 2007

We propose a group-based security scheme for hierarchical wireless sensor networks. We model the network for secure routing with 3-tier sensor network comprised of three types of nodes: Base Station, Group Dominator and ordinary Sensor Nodes. Group-based deployment is performed using Gaussian (normal) distribution and show that more than 85% network connectivity can be achieved with the proposed model. The small groups with pre-shared secrets form the secure groups where group dominators form the backbone of the entire network. The scheme is devised for dealing with sensory data aggregated by groups of collocated sensors; i.e., local sensed data are collected by the dominating nodes and sent an aggregated packet to the base station via other group dominators. The scheme is shown to be light-weight, and it offers a stronger defense against node capture attacks. Analysis and simulation results are presented to defend our proposal. Analysis shows that robustness can significantly be improved by increasing the deployment density using both the dominating and/or ordinary sensor nodes.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.93-109
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• 2013

Geostatistical data or point-referenced data have the information on the monitoring stations of interest where the observations are measured. Practical geostatistical data are obtained from a wide variety of observational monitoring networks that are mainly operated by the Korean government. When we analyze geostatistical data and predict the expectations at unobservable locations, we can improve the reliability of the prediction by utilizing some relevant spatial data obtained from different observational monitoring networks and blend them with the measurements of our main interest. In this paper, we consider the hierarchical spatial linear model that enables us to link spatial variables from different resources but with similar patterns and guarantee the precision of the prediction. We compare the proposed model to a classical linear regression model and simple kriging in terms of some information criteria and one-leave-out cross-validation. Real application deals with Sulfur Dioxide($SO_2$) measurements from the urban air pollution monitoring network and wind speed data from the surface observation network.