• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3496-3514
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• 2015

One of the challenging tasks in Mobile Ad hoc Network is to discover precise optimal routing solution due to the infrastructure-less dynamic behavior of wireless mobile nodes. Ant Colony Optimization, a swarm Intelligence technique, inspired by the foraging behaviour of ants in colonies was used in the past research works to compute the optimal path. In this paper, we propose a Recurrent Ant Colony Optimization (RECACO) that executes the actual Ant Colony Optimization iteratively based on recurrent value in order to obtain an optimal path convergence. Each iteration involves three steps: Pheromone tracking, Pheromone renewal and Node selection based on the residual energy in the mobile nodes. The novelty of our approach is the inclusion of new pheromone updating strategy in both online step-by-step pheromone renewal mode and online delayed pheromone renewal mode with the use of newly proposed metric named ELD (Energy Load Delay) based on energy, Load balancing and end-to-end delay metrics to measure the performance. RECACO is implemented using network simulator NS2.34. The implementation results show that the proposed algorithm outperforms the existing algorithms like AODV, ACO, LBE-ARAMA in terms of Energy, Delay, Packet Delivery Ratio and Network life time.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.73-78
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• 2016

In wireless sensor networks, sensors have capabilities of sensing and wireless communication, computing power and collect data such as sound, movement, vibration. Sensors need to communicate wirelessly to send their sensing data to other sensors or the base station and so they are vulnerable to many attacks like garbage packet injection that cannot be prevented by using traditional cryptographic mechanisms. To defend against such attacks, a behavior-based attack detection is used in which some specialized monitoring nodes overhear the communications of their neighbors(normal nodes) to detect illegitimate behaviors. It is desirable that the total sensing area of normal nodes covered by monitoring nodes is as large as possible. The previous researches have focused on selecting the monitoring nodes so as to maximize the number of normal nodes(node coverage), which does not guarantee that the area sensed by the selected normal nodes is maximized. In this study, we have developed an algorithm for selecting the monitoring nodes needed to cover the maximum sensing area. We also have compared experimentally the covered sensing areas computed by our algorithm and the node coverage algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.8
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• pp.335-342
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• 2013

Nowadays, the traffic type and behavior are extremely diverse due to the growth of network speed and the appearance of various services on Internet. For efficient network operation and management, the importance of application-level traffic identification is more and more increasing in the area of traffic analysis. In recent years traffic identification methodology using statistical features of traffic flow has been broadly studied. However, there are several problems to be considered in the identification methodology base on statistical features of flow to improve the analysis accuracy. In this paper, we recognize these problems by analyzing the ground-truth traffic and propose the solution of these problems. The four problems considered in this paper are the distance measurement of features, the selection of the representative value of features, the abnormal behavior of TCP sessions, and the weight assignment to the feature. The proposed solutions were verified by showing the performance improvement through experiments in campus network.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.8-17
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• 2002

This paper suggests that the immune algorithm can effectively be used in tuning of a PID controller. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes according to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems with a less flexible result to the external behavior. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. In addition to that, tuning performance cannot be guaranteed with regards to a plant with non-linear characteristics or many kinds of disturbances. Along with these, this paper used immune algorithm in order that a PID controller can be more adaptable controlled against the external condition, including moise or disturbance of plant. Parameters P, I, D encoded in antibody randomly are allocated during selection processes to obtain an optimal gain required for plant. The result of study shows the artificial immune can effectively be used to tune, since it can more fit modes or parameters of the PID controller than that of the conventional tuning methods.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.209-217
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• 2013

This study focuses on the analysis of purchased items' relationship occurred by consumers' purchasing behavior observed in on-line shopping mall based on social network analysis. In order to find relational characteristics of each item for establishing marketing strategy, we apply three definitions of centrality in network, which are degree centrality, betweenness centrality, and closeness centrality in the purchased items' network. Thus, the research results provide the criteria for selection of market segmentation variables. Furthermore, the details of case has been introduced to validate the analyzed results in terms of marketing strategy, and supporting evidences are provided accordingly.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.377-389
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• 2019

This study conducted social network big data analysis to investigate consumer's perception of Japanese sporting goods related to Japanese boycott and to extract problems and variables by recognition. Social network big data analysis was conducted in two areas, "Japanese boycott" and "Japanese sporting goods". Months of data were collected and investigated. If you specify the research method, you will identify the issues of the times - keyword setting using social network analysis - clustering using CONCOR analysis using TEXTOM and Ucinet 6 programs - variable selection through expert meetings - questionnaire preparation and answering - and validity of questionnaire Reliability Verification - It consists of hypothesis verification using the structural model equation. Based on the results of using the big data of social networks, four variables of relevant characteristics, nationality, attitude, and consumption behavior were extracted. A total of 30 questions and 292 questionnaires were used for final hypothesis verification. As a result of the analysis, first, the boycott-related characteristics showed a positive relationship with nationality. Specifically, all of the characteristics related to boycotts (necessary boycott, sense of boycott, and perceived boycott benefits were positively related to nationality. In addition, nationality was found to have a positive relationship with consumption behavior.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.150-152
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• 2022

In this work, we propose a model that considers the behavior and synaptic plasticity of sensory neurons based on Liquid Time-constant Network (LTC). The neuron connection structure was experimented with four types: the increasing number of neurons, the decreasing number, the decreasing number, and the decreasing number. In this study, we experimented using a time series prediction dataset to see if the performance of the changed model improved compared to LTC. Experimental results show that the application of modeling of sensory neurons does not always bring about performance improvements, but improves performance through proper selection of learning rules depending on the type of dataset. In addition, the connective structure of neurons showed improved performance when it was less than four layers.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.760-768
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• 2011

The performance of transmission lines and its shielding design during a lightning phenomenon are quite essential in the maintenance of a reliable power supply to consumers. The leader progression model, as an advanced approach, has been recently developed to calculate the shielding failure rate (SFR) of transmission lines using geometrical data and physical behavior of upward and downward lightning leaders. However, such method is quite time consuming. In the present paper, an effective method that utilizes artificial neural networks (ANNs) to create a metamodel for calculating the SFR of a transmission line based on shielding angle and height is introduced. The results of investigations on a real case study reveal that, through proper selection of an ANN structure and good training, the ANN prediction is very close to the result of the detailed simulation, whereas the Processing time is by far lower than that of the detailed model.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.159-161
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• 2006

The peer-to-peer (P2P) systems have Brown significantly over last few years due to their hish potential of sharing various resources. Super-peer based P2P systems have been found very effective by dividing the peers into two layers, SP (Super-Peer) and OP (Ordinary-Peer). In this paper, we present ISP2P (Intelligent Super-peer based P2P system), which allows us to choose the best SP. Through analyzing capacity and similarity between SP and OP, we can help OPs to select the most appropriate SP respectively. Proposed system can improve the performance of the average response time by superior SP, reduce the bandwidth cost by small path length due to content similarity and solve frequent SP replacement problem by considering similarity of user behavior.