• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.1
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• pp.123-128
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• 2010

Graph expression of attack scenarios is a necessary method for analysis of vulnerability in server as well as the design for defence against attack. Although various requirement analysis model are used for this expression, they are restrictive to express combination of complex scenarios. Role Based Petri Net suggested in this paper offer an efficient expression model based role on Petri Net which has the advantage of concurrency and visuality and can create unknown scenarios.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.3-8
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• 2016

Wireless networks have evolved to the appearance of heterogeneous wireless networks(HetNet), where various networks provide data services with various data rates and coverage. One of technical issues for HetNet is efficient utilization of radio resources for system performance enhancement. For the next generation wireless networks, energy saving has become one of key performance indices, so energy-efficient resource management schemes for HetNet need to be developed. This paper addresses an energy-efficient scheduling for HetNet in order to improve the energy efficiency while maintaining similar system throughput as existing scheme, for which an energy-efficient scheduling that energy efficiency factor is included. Simulation results show that the proposed scheme achieves the reduction of energy consumption while admitting limited ragne of throughput degradation in comparison with the conventional proportional fair scheduling.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.55-67
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• 2023

Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.

• Korean Journal of Computational Design and Engineering
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• v.10 no.3
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• pp.168-178
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• 2005

The Delaunay triangular net is primarily characterized by a balance of the whole by improving divided triangular patches into a regular triangle, which closely resembles an equiangular triangle. A triangular net occurring in certain, point-clustered, data is unique and can always create the same triangular net. Due to such unique characteristics, Delaunay triangulation is used in various fields., such as shape reconstruction, solid modeling and volume rendering. There are many algorithms available for Delaunay triangulation but, efficient sequential algorithms are rare. When these grids involve a set of points whose distribution are not well proportioned, the execution speed becomes slower than in a well-proportioned grid. In order to make up for this weakness, the ids are divided into sub-grids when the sets are integrated inside the grid. A method for finding a mate in an incremental construction algorithm is to first search the area with a higher possibility of forming a regular triangular net, while the existing method is to find a set of points inside the grid that includes the circumscribed sphere, increasing the radius of the circumscribed sphere to a certain extent. Therefore, due to its more efficient searching performance, it takes a shorer time to form a triangular net than general incremental algorithms.

• Journal of IKEEE
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• v.24 no.3
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• pp.685-690
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• 2020

We propose an efficient convolutional neural network with much lower computational complexity and higher accuracy based on MobileNet V2 for mobile or edge devices. The proposed network consists of bottleneck layers with larger expansion factors and adjusted number of channels, and excludes a few layers, and therefore, the computational complexity is reduced by half. The performance the proposed network is verified by measuring the accuracy and execution times by CPU and GPU using ImageNet100 dataset. In addition, the execution time on GPU depends on the CNN architecture.

• The Journal of Fisheries Business Administration
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• v.42 no.3
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• pp.31-39
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• 2011

This paper attempts to extend such analysis to the rather more difficult problem of optimal management of transnational fish stocks jointly owned by two countries. Transboundary fish such as Mackerel creates an incentive to harvest fish before a competitor does and leads to over-exploitation. This tendency is especially poignant for transnational stocks since, in the absence of an enforceable, international agreement, there is little or no reason for either government or the fishing industry to promote resource conservation and economic efficiency. In the current paper I examine a game theoretic setting in which cooperative management can provide more benefits than noncooperative management. A dynamic model of Mackerel fishery is combined with Nash's theory of two countries cooperative games. A characteristic function game approach is applied to describe the sharing of the surplus benefits from cooperation and noncooperation. A bioeconomic model was used to compare the economic yield of the optimal strategies for two countries, under joint maximization of net benefits in joint ocean. The results suggest as follows. First, the threat points represent the net benefits for two countries in absence of cooperation. The net benefits to Korea and China in threat points are 2,000 billion won(${\pi}^0_{KO}$) and 1,130 billion won(${\pi}^0_{CH}$). Total benefits are 3,130 billion won. Second, if two countries cooperate one with another, they reach the solution payoffs such as Pareto efficient. The net benefits to Korea and China in Pareto efficient are 2,785 billion won(${\pi}^0_{KO}$) and 1,605 billion won(${\pi}^0_{CH}$) or total benefits of 4,390 billion won : a gain of 1,260 billion won. Third, the different price effects under the two scenarios show that total benefit rise as price increases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.374-376
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• 2021

For the diagnosis of cancer-related diseases in clinical practice, pathological examination using biopsy is essential after basic diagnosis using imaging equipment. In order to proceed with such a biopsy, the assistance of an oncologist, clinical pathologist, etc. with specialized knowledge and the minimum required time are essential for confirmation. In recent years, research related to the establishment of a system capable of automatic classification of cancer cells using artificial intelligence is being actively conducted. However, previous studies show limitations in the type and accuracy of cells based on a limited algorithm. In this study, we propose a method to identify a total of 4 cancer cells through a convolutional neural network, a kind of deep learning. The optical images obtained through cell culture were learned through EfficientNet after performing pre-processing such as identification of the location of cells and image segmentation using OpenCV. The model used various hyper parameters based on EfficientNet, and trained InceptionV3 to compare and analyze the performance. As a result, cells were classified with a high accuracy of 96.8%, and this analysis method is expected to be helpful in confirming cancer.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.3
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• pp.147-156
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• 2008

A new approach based on cone prism intersection method combined with sorting algorithm is proposed for the fast and robust signed distance field computation. In the method, the space bounding the geometric model composed of triangular net is divided into multiple smaller cells. For the efficient calculation of distance fields, valid points among the triangular net which will generate minimum distances with current cell are selected by checking the intersection between current cell and cone prism generated at each point. The method is simple to implement and able to achieve an order of magnitude improvement in the computation time as compared to earlier approaches. Further the method is robust in handling the traditional sign problems. The validity of the suggested method was demonstrated by providing numerous examples including Boolean operation, shape deformation and morphing of complex geometric models.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.44-52
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• 2013

This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.175-182
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• 2021

Oral cancer is ranked second most diagnosed cancer among Indian population and ranked sixth all around the world. Oral cancer is one of the deadliest cancers with high mortality rate and very less 5-year survival rates even after treatment. It becomes necessary to detect oral malignancies as early as possible so that timely treatment may be given to patient and increase the survival chances. In recent years deep learning based frameworks have been proposed by many researchers that can detect malignancies from medical images. In this paper we have proposed a deep learning-based framework which detects oral cancer from histopathology images very efficiently. We have designed our model to split the color channels and extract deep features from these individual channels rather than single combined channel with the help of Efficient NET B3. These features from different channels are fused by using feature fusion module designed as a layer and placed before dense layers of Efficient NET. The experiments were performed on our own dataset collected from hospitals. We also performed experiments of BreakHis, and ICML datasets to evaluate our model. The results produced by our model are very good as compared to previously reported results.