• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1482-1484
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• 2010

A new TCP protocol can succeed for large bandwidth delay product when it meets network bandwidth utilization efficiency and fair sharing. We introduce a novel congestion control algorithm which employs queueing delay information in order to calculate the amount of congestion window increment in increase phase, and reduces congestion window to optimal estimated bound as packet loss occurs. Combination of such methods guarantees that the proposal utilizes fully network bandwidth, recovers quickly from packet loss in wireless link, and preserves fairness for competing flows mixed short RTT and long RTT. Our simulations show that features of the proposed TCP meet the desired requirements.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.464-469
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• 2023

As modern society develops, the number of diseases caused by bad smells is increasing. As it can harm people's health, it is important to predict in advance the extent to which bad smells may occur, inform the public about this, and take preventive measures. In this paper, we propose a hybrid neural network structure of CNN and LSTM that can be used to detect or predict the occurrence of odors, which are most required in manufacturing or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data, including hydrogen sulfide, ammonia, benzene, and toluene, in real time, and applies this data to the inference model to detect and predict the odor state. The proposed model evaluated the prediction accuracy of the training model through performance indicators based on accuracy, and the evaluation results showed an average performance of more than 94%.

• The Journal of Information Systems
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• v.33 no.1
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• pp.45-68
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• 2024

Purpose This paper examines the role of local market-based communities in driving regional revitalization, using detailed analysis of online networks. We aim to dissect a local community's communication network, highlighting members with high engagement levels and exploring their characteristics. Our goal is to identify the conditions that allow local community networks to grow independently and to demonstrate how the activation of these networks contributes to regional revitalization. Design/methodology/approach We employ a mixed-methods approach, combining social network analysis with statistical techniques to investigate the structure of online communication networks. Specifically, we use ANOVA to determine the statistical significance of our findings, ensuring their reliability. To complement our quantitative data, we include qualitative insights from interviews, adding depth and context to our analysis. Findings Our results show that individuals with high centrality in the online network are crucial for maintaining active local communities. We find that leveraging local resources to create a supportive and adaptable environment is essential for the communities' sustainability and expansion. Importantly, our research draws a direct connection between the vitality of local community networks and the broader process of regional revitalization. We argue that energizing local communities is an effective way to address the risk of regional decline. By integrating quantitative analysis with qualitative feedback, this study contributes to the understanding of local market-based communities as key drivers of regional development. It emphasizes the importance of building vibrant, resourceful community networks to revitalize areas experiencing socio-economic challenges.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.367-369
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• 2004

In this paper we propose an effective and reliable hand detection method using neural network with ICA(Independent Component Analysis) Features. Many algorithms of hand detection have been proposed yet. Among them, ICA is the one of the interesting topics in image processing. ICA can not only separate mixed signals but also efficiently extract low-dimensional features in signals. ICA features are able to represent the characteristic of the images well. The object of this paper is to use effectively ICA that has above advantage. That is, by the proper number of Independent component the arithmetic speed is faster and by normalization of ICA feature the performance of detection is more reliable. For this, we adopt the algorithm, the Proportion of variance, which select the ICA feature by comparing the ratio of variance of ICA feature. By this method, we can extract the feature that is good at classifying hand and non-hand. Our experimental results show that by using ICA features, we obtained a better performance in hand detection than by only training NN on the image. And we can use hand detection system effectively and reliably by our proposal.

• Journal of Korean Society of Transportation
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• v.17 no.2
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• pp.127-135
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• 1999

Most existing progression bandwidth models maximize the single or multi weighted sum of bandwidths in the both directions to improve traffic mobility on an arterial, but they cannot be applied to general networks. Even though a few models formulating a looped network problem cannot be applied to networks have not loops. Also they have some defects in optimizing phase sequences. Therefore, the objective of this study is to develope a mathematical formulation of the synchronization problem for a general traffic network. The goal is achieved successfully by introducing the signal phasing for each movement and expanding the mixed integer linear programming of MAXBAND. The experiments indicate that the proposed model can formulate the general traffic network problem mere efficiently than any other model. In conclusion, this model may optimize signal time to smooth progression in the general networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5631-5652
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• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.69-87
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• 2013

Recently a large number of people have been using social networking and microblogging services such as Facebook and Twitter. These mediums play a pivotal communication channel in a business-to-customer (B2C) relationship. Given its importance in today's business, companies have invested in the strategic application of social network services to reach out to customers. This study provides a blueprint for mechanisms for successful execution of social network services in the context of developing an effective B2C relationship, such as customer participation behavior. The S-O-R(Stimulus-Organism-Response) framework lays out the foundation for developing our research model and provides a structured view for understanding customer participation behavior on brand loyalty. For the methodology, this study employed a mixed-method approach. Additionally, in order to provide empirical evidences, a total of 121 respondents have completed the survey. All the data were compiled and analyzed through structural equation modeling and were implemented in partial least square (PLS). To sum up, this study presented theoretical and practical implications by providing the effect of customer participation behavior on brand loyalty through B2C microblogging.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4B
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• pp.455-463
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• 2004

In this paper, we deal with a ring-mesh network design problem arising from the deployment of WDM for the optical internet. The ring-mesh network consists of ring topology and full mesh topology for satisfying traffic demand while minimizing the cost of OAOMs and OXCs. The problem seeks to find an optimal clustering of traffic demands in the network such that the total number of node assignments is minimized, while satisfying ring capacity and node cardinality constraints. We formulate the problem as a mixed-integer programming model and prescribe a tabu search heuristic procedure Promising computational results within 3% optimality gap are obtained using the proposed method.

• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• Interdisciplinary Bio Central
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• v.4 no.3
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• pp.9.1-9.7
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• 2012

Filamentous phages have been in the limelight as a new type of nanomaterial. In this study, genetically and chemically modified fd phage was used to generate a biomimetic phage self-assembly product. Positively charged fd phage (p8-SSG) was engineered by conjugating 3-aminopropyltriethoxysilane (APTES) to hydroxyl groups of two serine amino acid residues introduced at the N-terminus of major coat protein, p8. In particular, formation of a phage network was controlled by changing mixed ratios between wild type fd phage and APTES conjugated fd-SSG phage. Assembled phages showed unique bundle and network like structures. The bacteriophage based self-assembly approach illustrated in this study might contribute to the design of three dimensional microporous structures. In this work, we demonstrated that the positively charged APTES conjugated fd-SSG phages can assemble into microstructures when they are exposed to negatively charged wild-type fd phages through electrostatic interaction. In summary, since we can control the phage self-assembly process in order to obtain bundle or network like structures and since they can be functionalized by means of chemical or genetic modifications, bacteriophages are good candidates for use as bio-compatible scaffolds. Such new type of phage-based artificial 3D architectures can be applied in tuning of cellular structures and functions for tissue engineering studies.