• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• The Journal of the Korea Contents Association
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• v.16 no.1
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• pp.190-203
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• 2016

Based on the premise that a systematic career process was one of the essential elements of successful task performance both for individuals and the organization in the field of social welfare, this study set out to empirically analyze factors influencing the career commitment of social workers at a multidimensional level and provide practical implications for the directionality of career management on the basis of data with theoretical and statistical accuracy. For those purposes, the study collected individual and organizational characteristics data from 787 social workers at 46 agencies through a structured questionnaire and analyzed influential factors through the multilevel analysis technique by taking organizational effects into account. The analysis results show that explanations by the organization characteristics recorded significant 15% in the total variance of career commitment and that its influential factors included such significant variables as the protean career attitude, desire for growth, human network, and self-efficacy at the individual level and also the qualification compensation system at the organizational level. The study then proposed and discussed integrated practice strategies between individuals and agencies as the measures to promote career success through the activation of individual factors based on the consideration of organizational effects such as the application of an employee assistant program, provision of incentives to professional career development, and shift to a learning organization.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.887-890
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• 1993

By introducing the notion of constraint-oriented fuzzy inference, we will show that it provides us ways of fuzzy control methods that has abilities of adaptation, learning and self-organization. The basic supporting techniques behind these abilities are“hard”processing by Artificial Intelligence or traditional computational framework and“soft”processing by Neural Network or Genetic Algorithm techniques. The reason that these techniques can be incorporated to fuzzy control systems is that the notion of“constraint”itself has two fundamental properties, that is, the“modularity”property due to its declarativeness and the“logicality”property due to its two-valuedness. From the former property, the modularity property, decomposing and integrating constraints can be done easily and efficiently, which enables us to carry out the above“soft”processing. From the latter property, the logicality property, Qualitative Reasoning and Instance Generalization by Symbolic Reasoning an be carried out, thus enabling the“hard”processing.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.62-64
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• 2004

P2P Systems have grown dramatically in recent years. Now many P2P systems have developed and been confronted by P2P technical challenges. We should consider how to efficiently locate desired resources. In this paper we integrated the existing pure P2P and hybrid P2P model. We try to keep roles of super peer in hybrid and concurrently use pure P2P model for searching resource. In order to improve the existing search mechanism, we present contents-based classification mechanism. Proposed system have the following features. This can forward only query to best peer using RI. Second, it is self-organization. A peer can reconfigure network that it can communicate directly with based on best peer. Third, peers can cluster each other through contents-based classification.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.9-19
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• 2008

This paper propose the issue of connecting nested NEMO (Network Nobility) networks to global IPv6 networks, while supporting IPv6 mobility. Specifically, we consider a self-addressing including topology information IPv6-enabled NEMO infrastructure. The proposed self-organization addressing protocol automatically organized mobile routers into free architecture and configuration their global IPv6 addresses. BU(binding update) to MR own HA and internal rouging, hosed on longest prefix matching and soft state routing cache, are specially designed for IPv6-based NEMO. In conclusion, numeric analysis ore conducted to show more efficiency of the proposed routing protocols than other RO (Route Optimization) approaches.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.1
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• pp.1-10
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• 2007

An advantage of the mobile ad hoc network (MANET) is that mobile nodes can self-organize the network topology without the help of network infrastructure. However, for the perfect self-organization of the MANET, each mobile node needs to self-configure its address. Even though a mobile node configures a unique address during the booting time, its address may conflict with nodes in other MANETs since MANETs containing the same address can be merged. The address autoconfiguration protocol implemented in this work consists of the strong DAD (Duplicate Address Detection) and the weak DAD. A unique address of a node is assigned by the strong DAD during the booting time and the weak DAD is used to detect address conflict and resolve address conflict during the ad hoc routing. In this work, we have implemented address autoconfiguration in the IPv6-based MANET using AODV as the routing protocol. We describe how the IPv6 address autoconfiguration is implemented and verify our implementation by showing the test scenarios on our testbed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.307-315
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• 2008

The aim of this study is to develop travel time estimation model by using Self-Organized Neural network(in brief, SON) algorithm. Travel time data based on vehicles equipped with GPS and number-plate matching collected from National road number 3 (between Jangji-IC and Gonjiam-IC), which is pilot section of National Highway Traffic Management System were employed. We found that the accuracies of travel time are related to location of detector, the length of road section and land-use properties. In this paper, we try to develop travel time estimation using SON to remedy defects of existing neural network method, which could not additional learning and efficient structure modification. Furthermore, we knew that the estimation accuracy of travel time is superior to optimum located detectors than based on existing located detectors. We can expect the results of this study will make use of location allocation of detectors in highway.

• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.393-402
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• 2015

Human resources are one of the most essential elements of an organization. In particular, the more closed a group is, the higher the value each member has. Previous studies have focused on personal attributes of individual, such as medical history, and have depended upon self-diagnosis to manage structures. However, this method has weak points, such as the timeconsuming process required, the potential for concealment, and non-disclosure of participants' mental states, as this method depends on self-diagnosis through extensive questionnaires or interviews, which is solved in an interactive way. It also suffers from another problem in that relations among people are difficult to express. In this paper, we propose a multi-faced diagnosis model based on social network analysis which overcomes former weaknesses. Our approach has the following steps : First, we reveal the states of those in a social network through 9 questions. Next, we diagnose the social network to find out specific individuals such as victims or leaders using the proposed algorithm. Experimental results demonstrated our model achieved 0.62 precision rate and identified specific people who are not revealed by the existing methods.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.13-21
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• 1995

In this paper, we improve an image classifier algorithm based on neural network learning. It consists of two steps. The first is input pattern generation and the second, the global neural network implementation using an improved back-propagation algorithm. The feature vector for pattern recognition consists of the codebook data obtained from self-organization feature map learning. It decreases the input neuron number as well as the computational cost. The global neural network algorithm which is used in classifier inserts a control part and an address memory part to the back-propagation algorithm to control weights and unit-offsets. The simulation results show that it does not fall into the local minima and can implement easily the large-scale neural network. And it decreases largely the learning time.