• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.98-101
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• 2004

Linear Programming(LP) is the term used for defining a wide range of optimization problems in which the objective function to be minimized or maximized is linear in the unknown variables and the constraints are a combination of linear equalities and inequalities. LP problems occur in many real-life economic situations where profits are to be maximized or costs minimized with constraint limits on resources. While the simplex method introduced in a later reference can be used for hand solution of LP problems, computer use becomes necessary even for a small number of variables. Problems involving diet decisions, transportation, production and manufacturing, product mix, engineering limit analysis in design, airline scheduling, and so on are solved using computers. This technique is called Sequential Linear Programming (SLP). This paper describes LP's problems and solves a LP's problems using the neural networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1735-1754
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• 2022

The reliability and performance of WiFi need optimization because of the rising number of WiFi users day by day. A highlighted point is saving power while transmitting and receiving packets using WiFi devices. Wake-on-Wlan (WoW) is also implemented to improve energy consumption, but it also needs betterment. This paper will introduce universal ideas to transmit and receive packets using low-power technology like Bluetooth or BLE (Bluetooth low energy). While looking for power-saving ways in this research, WiFi connection and maintenance also take care using lesser power-consuming technology. Identifying different use-cases where low power technology can help save energy and maintain 802.11 connection is part of the research. In addition, the proposed method discuss energy saving with unicast and broadcast/multicast data. Calculation of power-saving and comparison with standalone WiFi usage clearly shows the effectiveness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2763-2771
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• 1997

To determine the satellite orbital positions considering interference caused by inter-satellite systems is one of the most improtant issues in terms of optimal usage of satellite network resources. In this paper, considering ITU filing situation, we present the satellite orbital positions determination method to minimize iter-satellite system interference effect in the fixed satellite communication using an optimization method. Through the computer simulatio, it was shown that the proposed method is suitable to determine the satellite orbital positions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1563-1566
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• 2022

Video streaming applications such as Netflix and Youtube are widely used in our daily life. A DASH based streaming client exploits adaptive bit rate (ABR) method to choose the most appropriate video source representation that the network can support. In this paper we propose a novel energy-aware ABR scheme that adds the ability to monitor energy efficiency in addition to the linear quadratic regulator algorithm we previously introduced. Our trace-driven simulation studies show that our proposed scheme mitigates and shortens re-buffering, resulting in energy savings of mobile devices while preserving the similar QoE compared to the state-of-the-art ABR algorithms.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.1-11
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• 2024

Cognitive small cell networks, consisting of macro-cells and small cells, are foreseen as a promising candidate solution to address 5G spectrum scarcity. Recently, many technological issues (such as spectrum sensing, spectrum sharing) related to cognitive small cell networks have been studied, but the common control channel (CCC) establishment problem has been ignored. CCC is an indispensable medium for control message exchange that could have a huge significant on transmitter-receiver handshake, channel access negotiation, topology change, and routing information updates, etc. Therefore, establishing CCC in cognitive small cell networks is a challenging problem. In this paper, we propose a potential game theory-based approach for CCC establishment in cognitive radio networks. We design a utility function and demonstrate that it is an exact potential game with a pure Nash equilibrium. To maintain the common control channel list (CCL), we develop a CCC update algorithm. The simulation results demonstrate that the proposed approach has good convergence. On the other hand, it exhibits good delay and overhead of all networks.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.420-424
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• 2005

정보 통신 기술의 발달로 엄청난 양의 정보가 World Wide Web을 통해 저장되고 공유된다. 웹 정보의 양이 커질수록 이의 구조화 노력은 점증될 수밖에 없다. 본 논문의 목적은 웹을 유향그래프로 인식하고, 특히 웹 사이트에 초점을 맞추어 웹의 시작페이지(default page)와 이를 제외한 모든 페이지에 대해 최적구조화를 수행하되, 각 개별 웹 페이지를 하나의 종점(terminal page)으로 정의하고, 시작 페이지로부터 각 페이지로의 최적의 경로를 찾아내면서 전체 site의 비용을 최소화할 수 있는 구조를 탐색하는 것이다. 또한 라그랑지안 릴렉세이션을 적용하여 경로 제약조건의 변화에 대해 효율적인 최적해의 변화를 검증하며, 웹의 구조적분석에 적합한지 여부를 적용하는 것이다. 본 연구에서는 웹에 대해 최적화 모델링을 입안 및 분석하였으며, 실험으로서 입안된 모델을 최적화 툴에 적용하여 최적구조화에 부합되는 결과를 얻을 수 있음을 입증하였다.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2627-2640
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• 1998

In this paper, we analyze the performance of the virtual cell system[1] for the transmission of IP datagrams in mobile computer communications. A virtual cell consistsof a group of physical cells shose base stationsl are implemented b recote bridges and interconnected via high speed datagram packet switched networks. Host mobility is supported at the data link layer using the distributed hierachical location information of mobile hosts. Given mobility and communication ptems among physical cells, the problem of deploying virtual cells is equivalent to the optimization cost for the entire system where interclster communication is more expesive than intracluster communication[2]. Once an iptimal partitionof disjoint clusters is obtained, we deploy the virtual cell system according to the topology of the optimal partition such that each virtual cell correspods to a cluser. To analyze the performance of the virtual cell system, we adopt a BCMP open multipel class queueing network model. In addition to mobility and communication patterns, among physical cells, the topology of the virtual cell system is used to determine service transition probabilities of the queueing network model. With various system parameters, we conduct interesting sensitivity analyses to determine network design tradeoffs. The first application of the proposed model is to determine an adequate network bandwidth for base station networking such that the networks would not become an bottleneck. We also evaluate the network vlilization and system response time due to various types of messages. For instance, when the mobile hosts begin moving fast, the migration rate will be increased. This results of the performance analysis provide a good evidence in demonsratc the sysem effciency under different assumptions of mobility and communication patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3519-3533
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• 2020

Tuberculosis is a chronic and delayed infection which is easily experienced by young people. According to the statistics of the World Health Organization (WHO), there are nearly ten million fell ill with tuberculosis and a total of 1.5 million people died from tuberculosis in 2018 (including 251000 people with HIV). Tuberculosis is the largest single infectious pathogen that leads to death. In order to help doctors with tuberculosis diagnosis, we compare the tuberculosis classification abilities of six popular convolutional neural network (CNN) models in the same data set to find the best model. Before training, we optimize three parts of CNN to achieve better results. We employ sigmoid function to replace the step function as the activation function. What's more, we use binary cross entropy function as the cost function to replace traditional quadratic cost function. Finally, we choose stochastic gradient descent (SGD) as gradient descent algorithm. From the results of our experiments, we find that Densenet121 is most suitable for tuberculosis diagnosis and achieve a highest accuracy of 0.835. The optimization and expansion depend on the increase of data set and the improvements of Densenet121.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.