• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.280-285
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• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself. Through this process, we comfirmed advantages/disadvantages of four algorithms and identified application methods of four algorithms.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.108-115
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• 2000

This paper presents the new simulation algorithms using network methods to analysis the steady-state train voltage distribution characteristics in ac auto-transformer fed railroads. In general, the supply system of railroads is composed of non-symmetrical and unbalance transmission line. Therefore, the general method using simplified old algorithms have the self-contradictory errors because the supply line of train railroads is completely unbalanced. In this paper, the simulation results of new developed algorithms is compared with those of EMTP to confirm the effectiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.67-79
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• 1997

In this paper we develop the integrated software that can compare algorithms of the minimum cost flow problem using PC. The chosen algorithms are the network simplex method, dual network simplex method, and out-of-kilter method, which methods correspond to primal, dual, and primal-dual approach respectively. We also present the improved methods obtaining the initial solution to increase the efficiency of algorithms, and experiment results shown the difference between the entering(dropping) selection rules.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.27-36
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• 1995

This paper covers algorithms to determine a machine assignment strategy to locations on a single straight track by minimizing the total backtrack distance. Three different algorithms ar presented: an efficient heuristic procedure, the branch-and-bound algorithm, and the nerual network approach. Simulation results show that the proposed algorithms have potential power to design an on-line optimizer.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.165-170
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• 2023

Scheduling algorithms plays a significant role in optimizing the CPU in operating system. Each scheduling algorithms schedules the processes in the ready queue with its own algorithm design and its properties. In this paper, the performance analysis of First come First serve scheduling, Non preemptive scheduling, Preemptive scheduling, Shortest Job scheduling and Round Robin algorithm has been discussed with an example and the results has been analyzed with the performance parameters such as minimum waiting time, minimum turnaround time and Response time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.195-204
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• 2003

Network optimization is being increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Networks provide a useful way to modeling real world problems and are extensively used in practice. Many real world applications impose on more complex issues, such as, complex structure, complex constraints, and multiple objects to be handled simultaneously and make the problem intractable to the traditional approaches. Recent advances in evolutionary computation have made it possible to solve such practical network optimization problems. The invited talk introduces a thorough treatment of evolutionary approaches, i.e., hybrid genetic algorithms approach to network optimization problems, such as, fixed charge transportation problem, minimum cost and maximum flow problem, minimum spanning tree problem, multiple project scheduling problems, scheduling problem in FMS.

• ETRI Journal
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• v.29 no.6
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• pp.820-822
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• 2007

ARIA and the Advanced Encryption Standard (AES) are next generation standard block cipher algorithms of Korea and the US, respectively. This letter presents an area-efficient unified hardware architecture of ARIA and AES. Both algorithms have 128-bit substitution permutation network (SPN) structures, and their substitution and permutation layers could be efficiently merged. Therefore, we propose a 128-bit processor architecture with resource sharing, which is capable of processing ARIA and AES. This is the first architecture which supports both algorithms. Furthermore, it requires only 19,056 logic gates and encrypts data at 720 Mbps and 1,047 Mbps for ARIA and AES, respectively.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.3
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• pp.39-44
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• 2006

In this study, we nodestructive test based on ultrasonic test as inspection method and compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as pattern recognition algorithm of welding flasw. For this purpose, variables are applied the same to two algorithms. Where, feature variables are zooming flaw signals of reflected whole signals from welding flaws in time domain. Through this process, we confirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.