• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.87-94
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• 2019

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.160-160
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• 2022

Floods are the most common natural disasters and are annually causing severe destructions worldwide. Human activities, along with expected increased extreme precipitation patterns as a result of climate change enhance the future potential of floods. There are proven evidence that infrastructure based responses to flood disaster is no longer achieving optimum mitigation and have created a false sense of security. Nature-based solutions(NBS) is a widely accepted sustainable and efficient approach for disaster risk reduction and involves the protection, restoration, or management of natural and semi-natural ecosystems to tackle the climate and natural crisis. Adoption of NBS in decision-making, especially in developing nations is limited due to a lack of sufficient scenario-based studies, research, and technical knowledge. This study explores the knowledge gap and challenges on NBS adoption with case study of developing nation, specially for flood management, by the study of multiple scenario analysis in the context of climate, land-use change, and policies. Identification and quantification of the strength of natural ecosystems for flood resilience and water management can help to prioritize NBS in policymaking leading to sustainable measures for integrated flood management.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.649-660
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• 1998

Steel making in Mini Mill consists of three major processing stages: molten steel making in an electric arc fuenace, slab casting in a continuous caster, and hot rolling in a finishing mill. Each processing stage has its own lot grouping criterion. However, these criteria in three stages are conflicting with each other. Therefore, delveloping on efficient lot grouping algorithm to enhance the overall productivity of the Mini Mill is an extremely difficult task. The algorithm proposed in this paper is divided into three steps hierarchically: change grouping, cast grouping, and roll grouping. An efficient charge grouping heuristic is developed by exploiting the characteristics of the orders, the processing constraints and the requirements for the downstream stages. In order to maximaize the productivity of the continuous casters, each cast must contain as many charges as possible. Based on the constraint satisfaction problem technique, an efficient cast grouping heuristic is developed. Each roll consists of two casts satisfying the constraints for rolling. The roll grouping problem is formulated as a weighted non-bipartite matching problem, and an optimal roll grouping algorithm is developed. The proposed algorithm is programmed with C language and tested on a SUN Workstation with real data obtained from the H steel works. Through the computational experiment, the algorithm is verified to yield quite satisfactory solutions within a few minutes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.147-165
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• 2021

In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.469-474
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• 2021

Nowadays, a lot of software engineers struggle with the efficient back-end design of mobile application programs operated on the new mobile platform. It is simply because not only their lack of experiences in developing large scale system but also the unstructured nature of the mobile software, where there are no standard solutions. Furthermore, since big data is at the center of many challenges in system design of mobile software, so an efficient system design scheme is required for the development of such data-intensive applications. In this paper, we propose a systematic and efficient system design method that can figure out the substantial nature of the mobile software and solve the difficulties of the back-end software engineers.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.89-96
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• 2021

This work is an extension of my work presented a robust and economically efficient method for the Discrimination of four Mung-Beans [1] varieties based on quantitative parameters. Due to the advancement of technology, users try to find the solutions to their daily life problems using smartphones but still for computing power and memory. Hence, there is a need to find the best classifier to classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. To achieve this study's goal, we take the experiments on various supervised classifiers with simple architecture and calculations and give the robust performance on the most relevant 10 suggested features selected by Fisher Co-efficient, Probability of Error, Mutual Information, and wavelet features. After the analysis, we replace the Artificial Neural Network and Deep learning with a classifier that gives approximately the same classification results as the above classifier but is efficient in terms of resources and time complexity. This classifier is easily implemented in the smartphone environment.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.47-54
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• 2024

With the rapid development of 3D animation, MetaHuman is widely used in film production, game development and VR production as a virtual human creation platform.In the animation production of virtual humans, motion capture is usually used.Since different motion capture solutions use different skeletons for motion recording, when the skeleton level of recorded animation data is different from that of MetaHuman, the animation data recorded by motion capture cannot be directly used on MetaHuman. This requires Reorient the skeletons of both.This study explores an efficient skeleton reorientation method that can maintain the accuracy of animation data by reducing the number of bone chains.In the experiment, three skeleton structures, Rokoko, Mixamo and Xsens were used for efficient redirection experiments, to compare and analyze the adaptability of different skeleton structures to the MetaHuman skeleton, and to explore which skeleton structure has the highest compatibility with the MetaHuman skeleton.This research provides an efficient skeleton reorientation idea for the production team of 3D animated video content, which can significantly reduce time costs and improve work efficiency.

• ETRI Journal
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• v.35 no.1
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• pp.173-176
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• 2013

Existing symmetric cryptography-based solutions against pollution attacks for network coding systems suffer various drawbacks, such as highly complicated key distribution and vulnerable security against collusion. This letter presents a novel homomorphic subspace message authentication code (MAC) scheme that can thwart pollution attacks in an efficient way. The basic idea is to exploit the combination of the symmetric cryptography and linear subspace properties of network coding. The proposed scheme can tolerate the compromise of up to r-1 intermediate nodes when r source keys are used. Compared to previous MAC solutions, less secret keys are needed for the source and only one secret key is distributed to each intermediate node.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.4
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• pp.941-949
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• 1995

An efficient method based on analytic solutions is applied to solve anti-plane Mode III crack problems. The analytic technique developed earlier by the present authors for Laplace's equation in a simply-connected region is now extended to general Mode III crack problems. Unlike typical numerical methods which require fine meshing near crack tips, the present method divides the cracked bodies, typically non-convex or multiply-connected, into only a few super elements. In each super element, an element stiffness matrix, relating the series coefficients of the traction and displacement, is first formed. Then an assembly algorithm similar to that used in the finite elements, is first formed. Then an assembly algorithm similar to that used in the finite elements, is developed. A big advantage of the present method is that only the boundary conditions are to be satisfied in the solution procedure due to the use of analytic solutions. Several numerical results demonstrate the efficiency and accuracy of the present method.