• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Information Technology Applications and Management
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• v.9 no.3
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• pp.97-111
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• 2002

This paper attempts to provide an overview of relationship between the characteristics of the network and competitive strategies. We review the theoretical background of the efficiency of network, which Is very important for the network-based industries. Network externality, positive feedback effects, bandwagon effects, economies of scale, economies of scope in network-base businesses are reviewed. Various network situations, including interconnection, and strategies are also discussed. In this purpose, simple but meaningful examples and cases are used to show the economic goals and means of network competition strategies. We try to link network strategies to the generic strategies and coopetition suggested by Porter and by Brandenburger and Nalebuff respectively. Since this study is an exploratory research, further studies on more complex network situation in the real work can be executed with taking advantage of this effort.

• Journal of Communications and Networks
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• v.12 no.1
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• pp.11-18
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• 2010

Diversity-multiplexing tradeoff (DMT) is an efficient tool to measure the performance of multiple-input and multiple-output (MIMO) systems and cooperative systems. Recently, cooperative multicast system with wireless network coding stretched tremendous interesting due to that it can drastically enhance the throughput of the wireless networks. It is desirable to apply DMT to the performance analysis on the multicast system with wireless network coding. In this paper, DMT is performed at the three proposed wireless network coding protocols, i.e., non-regenerative network coding (NRNC), regenerative complex field network coding (RCNC) and regenerative Galois field network coding (RGNC). The DMT analysis shows that under the same system performance, i.e., the same diversity gain, all the three network coding protocols outperform the traditional transmission scheme without network coding in terms of multiplexing gain. Our DMT analysis also exhibits the trends of the three network coding protocols' performance when multiplexing gain is changing from the lower region to the higher region. Monte-Carlo simulations verify the prediction of DMT.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.1
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• pp.1-7
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• 2011

The nature of a network implies movement among vertices, and can be regarded as flows. Based on the flow concept which network follows the hydraulic fluid principle, we develop a spatial network model using Bernoulli equation. Then we explore the organization of spatial network and growth by the velocity of network flows. Results show that flow velocity determines network connections or influence of a vertex up to a point, and that the overall network structure is the result of pull force (pressure) and flow velocity. We demonstrate how one vertex can monopolize connections within a network.

• Journal of information and communication convergence engineering
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• v.2 no.4
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• pp.237-242
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• 2004

In this paper, we introduce a complex systems synchronization method using hyper-chaos circuit consist of State-Controlled Cellular Neural Network (SC-CNN). We make a complex systems using SC-CNN with the n-double scroll. A complex system is created by applying identical n-double scroll or non-identical n-double scroll and Chua's oscillator with weak coupled method to each cell. Complex systems synchronization were achieved using GS(Generalized Synchronization) method between the transmitter and receiver about each state variable in the SC-CNN.

• BMB Reports
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• v.54 no.5
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• pp.246-252
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• 2021

The Golgi complex plays a central role in protein secretion by regulating cargo sorting and trafficking. As these processes are of functional importance to cell polarity, motility, growth, and division, there is considerable interest in achieving a comprehensive understanding of Golgi complex biology. However, the unique stack structure of this organelle has been a major hurdle to our understanding of how proteins are secreted through the Golgi apparatus. Herein, we summarize available relevant research to gain an understanding of protein secretion via the Golgi complex. This includes the molecular mechanisms of intra-Golgi trafficking and cargo export in the trans-Golgi network. Moreover, we review recent insights on signaling pathways regulated by the Golgi complex and their physiological significance.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.119-128
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• 2013

The spotlight is thrown on the ubiquitous technology these days, and diverse management technologies are proposed to efficiently manage the networks in the ubiquitous environment. Because each network has its unique management technology, the integrated management of complex networks is a very difficult challenge. In this paper, an integrated network management methodology is proposed to ensure the efficient management of different networks using ontology. Although the proposed integrated network management methodology is quite simple, the definition of this methodology is essential for the integrated network management. Using the $Prot\acute{e}g\acute{e}$ to Ontology development, the terms for the integrated network management were defined, along with the OWL and relevant rules, and several methods were implemented according to the proposed methodology. The process in this paper is considered essential for the network expansion and multiple network management.

• Korean Journal of Agricultural Science
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• v.39 no.2
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• pp.263-270
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• 2012

The vitalization of Agro-industrial complex could be an alternative to enhance farmers off-farm income and overcome FTA's impacts on the import of agricultural products. However, it is evaluated the governmental policy on the Agro-industrial complex has not been successfully done as expected. The Agro-industrial complex started to be composed in 1984 and the 427 Agro-industrial complex have been composed nationwide by 2011. Meanwhile, three Agro-industrial complex where 61 factories are running have been composed and two Agro-industrial complex are being under construction in Seocheongun as of 2011. It is investigated that there are constraints in the government supporting systems for Agro-industrial complex. They are; Aged and poor infrastructure facilities, Weak marketing competition, Scattered supporting agencies, and Poor loan system, etc. In this respect, the policy of supporting system was suggested and recommended to vitalize Agro-industrial complex in the study. They are; Better loan system, Improving aged facilities, Marketing support, Assisting technology, Suppling labor power, and Forming factory-network, etc.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.253-263
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• 2021

Machine learning is the process of constructing a cost function using learning data used for learning and an artificial neural network to predict the data, and finding parameters that minimize the cost function. Parameters are changed by using the gradient-based method of the cost function. The more complex the digital signal and the more complex the problem to be learned, the more complex and deeper the structure of the artificial neural network. Such a complex and deep neural network structure can cause over-fitting problems. In order to avoid over-fitting, a weight decay regularization method of parameters is used. We additionally use the value of the cost function in this method. In this way, the accuracy of machine learning is improved, and the superiority is confirmed through numerical experiments. These results derive accurate values for a wide range of artificial intelligence data through machine learning.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.364-371
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• 2009

We analyzed romance of the three kingdoms written by Luo Guanzhong by using complex network to compare its properties with those of social network. The three kingdom network has a scale free and hierarchical network properties. Human-human interaction networks are dramatically changing in response to time and space. Due to lack of the dynamic interacting data, the researches only focus to analysis for properties of the statical networks. The romance of the three kingdoms is a Chinese historical novel based upon events from the end of the Han Dynasty, the Three Kingdoms era of China, and the reunification of the land. There are over a thousand characters, over a three thousand human-human interactions, and the dynamic changed in human-human interactions in the historical novel. Here, we introduce that a possible method for analyzing about dynamic changing of the complex network.