• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.9-10
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• 2007

[1],[2]의 드래프트 문서에서는 Mobile Network 내부에 HA를 두고 이동하는 Mobile Node를 LMN(Local Mobile Node)라 정의하고 있다. NEMO Basic Support를 기반으로 Nested Mobile Network에서 LMN의 이동에 대한 패킷 전송 경로를 가정 할 때 이 경로는 일반적인 Nested NEMO의 경우 보다 상당히 복잡한 경로를 가지게 된다. 본 논문에서는 이러한 LMN이 이동할 경우 패킷 전송경로에 대해 분석하고, Nested NEMO에 MANET을 적용하여 LMN의 이동에 대한 경로 최적화 방안을 제안하고자 한다.

• Transactions on Electrical and Electronic Materials
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• v.7 no.4
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• pp.210-214
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• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1356-1358
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• 1996

Hopfield's suggestion of a neural network model for associative memory aroused the interest of many scientists and led to efforts of mathematical analyses. But the Hopfield Network has several disadvantages such as spurious states and capacity limitation. In that sense many scientists and engineers are trying to use a new optimization algorithm called genetic algorithm. But it is hard to use this algorithm in Hopfileld Network because of the fixed architecture. In this paper we introduce another method to determine the weight of Hopfield type network using Genetic Algorithm.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1730-1738
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• 2008

Quality of Service (QoS) Guarantees grant ways for service providers to establish service differentiation among subscribers. On the other hand, service subscribers are also assured the level of service they paid for. In addition, the efficient level of service quality can be selected according to the subscribers' needs thus ensuring efficient use of available bandwidth. While network utility optimization techniques assure certain QoS metrics, a number of situations exist where some QoS goals are not met. The optimality of the network parameters is not mandatory to guarantee specified QoS levels. This paper proposes a joint data rate and power control scheme that guarantees service contract QoS level to a subscriber using Goal Programming. In using goal programming, this paper focuses on finding the range of feasible solutions as opposed to solving for the optimal. In addition, in case no feasible solution is found, an acceptable compromised solution is solved.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.519-527
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• 2021

Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

• Electronics and Telecommunications Trends
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• v.38 no.2
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• pp.26-35
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• 2023

Energy efficiency is an important factor toward sustainable future mobile network systems. As the size of the 5G mobile network system increases, the consumption and costs of energy increase. Accordingly, energy-saving optimization has become a major process in network systems, and various related technologies for energy saving are being developed. We provide a brief review of the technical trends in energy saving in 3GPP 5G & 5G Advanced systems and O-RAN systems. We focus on power models and energy-saving technologies in various resource domains of 3GPP 5G & 5G Advanced systems and energy-saving use cases of O-RAN systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.561-562
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• 2021

CNN (Convolutional Neural Network) is widely used to recognize various images. In this presentation, a single digit handwritten by humans was recognized by applying the CNN technique of deep learning. The deep learning network consists of a convolutional layer, a pooling layer, and a platen layer, and finally, we set an optimization method, learning rate and loss functions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2395-2410
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• 2013

Many telecommunication operators around the world have multiple networks. The networks run by each operator are always of different generations, such as 2G and 3G or even 4G systems. Each system has unique characters and specified requirements for optimal operation. It brings about resource allocation problem among these networks for the operator, because the budget of each operator is limited. However, the evaluation of resource allocation among various networks under each operator is missing for long, not to mention resource allocation optimization. The operators are dying for an algorithm to end their blind resource allocation, and the Resource Allocation Optimization Algorithm for Multi-network Operator (RAOAMO) proposed in this paper is what the operators want. RAOAMO evaluates and optimizes resource allocation in the view of overall cost for each operator. It outputs a resource distribution target and corresponding optimization suggestion. Evaluation results show that RAOAMO helps operator save overall cost in various cases.