• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4108-4122
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• 2019

Workflow process mining is becoming a more and more valuable activity in workflow-supported enterprises, and through which it is possible to achieve the high levels of qualitative business goals in terms of improving the effectiveness and efficiency of the workflow-supported information systems, increasing their operational performances, reducing their completion times with minimizing redundancy times, and saving their managerial costs. One of the critical challenges in the workflow process mining activity is to devise a reasonable approach to discover and recognize the bottleneck points of workflow process models from their enactment event histories. We have intuitively realized the fact that the iterative process pattern of redo-activities ought to have the high possibility of becoming a bottleneck point of a workflow process model. Hence, we, in this paper, propose an algorithmic approach and its implementation to discover the redo-activities and their performers' involvements patterns from workflow process enactment event logs. Additionally, we carry out a series of experimental analyses by applying the implemented algorithm to four datasets of workflow process enactment event logs released from the BPI Challenges. Finally, those discovered redo-activities and their performers' involvements patterns are visualized in a graphical form of information control nets as well as a tabular form of the involvement percentages, respectively.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

• ETRI Journal
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• v.43 no.3
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• pp.483-510
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• 2021

In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in O (N) time, whereas quantum algorithm design is based on Grover's method, which completes the search in $O(\sqrt{N})$ time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum-based combined exact (QBCE) algorithm for the pattern-matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing-based exact (QPBE) pattern-matching algorithm. We show that to find all t occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are $O(\sqrt{t})$ and $O(\sqrt{N})$, and the exceptional worst case is bounded by O (t) and O (N). Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern-matching methods.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1491-1510
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• 2021

This work presents an illustration of Nagra's AMAC (Advanced Methodology for Activation Characterization) approach to the South Korean pressurized water reactor Kori-1 decommissioning. The results achieved are supported by the existing experience from the detailed AMAC applications to Swiss NPPs and are used not only for a demonstration of the applicability of AMAC to South Korean NPPs, but also for a first approximation of the activated waste volumes to be expected from Kori-1. A packaging concept based on the above activation characterization is also presented, using the AMAC algorithmic optimization software ALGOPACK leading to the minimum number of waste containers needed given the selected packaging constraints. Nagra's AMAC enables effective planning before and during NPP decommissioning, including recommendations for cutting profiles for diverse reactor components and building structures. Finally, it is expected to lead to significant cost savings by reducing the number of expensive waste containers, by optimizing a potential melting strategy for metallic waste as well as by significantly limiting the number of radiological measurements. All information about Kori-1 used for the purpose of this study was collected from publicly available sources.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.295-305
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• 2022

In the modern artificial intelligence society, the goal of education is to cultivate convergent talents who can adapt well to and lead the artificial intelligence society. The core competency required of convergent future talents is to be able to understand and create software well. In this respect, algorithmic education is very important. Therefore, in this study, an algorithm education program was developed to improve convergent thinking ability. The effectiveness was verified through the paired samples t-test by conducting a pre-post test of convergent thinking ability, and it was found that students' convergent thinking ability was significantly improved in the areas of algorithm knowledge and function.

• Bulletin of the Korean Mathematical Society
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• v.60 no.3
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• pp.785-827
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• 2023

In this paper, under some new appropriate conditions imposed on the parameter and mappings involved in the resolvent operator associated with a P-accretive mapping, its Lipschitz continuity is proved and an estimate of its Lipschitz constant is computed. This paper is also concerned with the construction of a new iterative algorithm using the resolvent operator technique and Nadler's technique for solving a new system of generalized multi-valued resolvent equations in a Banach space setting. The convergence analysis of the sequences generated by our proposed iterative algorithm under some appropriate conditions is studied. The final section deals with the investigation and analysis of the notion of H(·, ·)-co-accretive mapping which has been recently introduced and studied in the literature. We verify that under the conditions considered in the literature, every H(·, ·)-co-accretive mapping is actually P-accretive and is not a new one. In the meanwhile, some important comments on H(·, ·)-co-accretive mappings and the results related to them appeared in the literature are pointed out.

• Steel and Composite Structures
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• v.49 no.4
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• pp.407-417
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• 2023

After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. Many domain schemes based on the measured vibration computations, including least squares estimation and neural fuzzy logic control, have been studied and found to be effective for online/offline monitoring of structural damage. Traditional strategies require all external stimulus data (input data) which have been measured available, but this may not be the generalized for all structures. In this article, a new method with unknown inputs (excitations) is provided to identify structural matrix such as stiffness, mass, damping and other nonlinear parts, unknown disturbances for example. An analytical solution is thus constructed and presented because the solution in the existing literature has not been available. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3163-3181
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• 2023

Eavesdropping attacks have seriously threatened network security. Attackers could eavesdrop on target nodes and link to steal confidential data. In the traditional network architecture, the static routing path and the important nodes determined by the nature of network topology provide a great convenience for eavesdropping attacks. To resist monitoring attacks, this paper proposes a random routing mutation defense method based on dynamic path weight (DPW-RRM). It utilizes network centrality indicators to determine important nodes in the network topology and reduces the probability of important nodes in path selection, thereby distributing traffic to multiple communication paths, achieving the purpose of increasing the difficulty and cost of eavesdropping attacks. In addition, it dynamically adjusts the weight of the routing path through network state constraints to avoid link congestion and improve the availability of routing mutation. Experimental data shows that DPW-RRM could not only guarantee the normal algorithmic overhead, communication delay, and CPU load of the network, but also effectively resist eavesdropping attacks.

• Advances in aircraft and spacecraft science
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• v.11 no.1
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• pp.33-53
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• 2024

This paper presents a comprehensive investigation into the optimization of Flight Management Systems (FMS) with a particular emphasis on data processing efficiency by conducting a comparative study with conventional methods to edge-computing technology. The objective of this research is twofold. Firstly, it evaluates the performance of FMS navigation systems using conventional and edge computing methodologies. Secondly, it aims to extend the boundaries of knowledge in edge-computing technology by conducting a rigorous analysis of terrain data and its implications on flight path optimization along with communication with ground stations. The study employs a combination of simulation-based experimentation and algorithmic computations. Through strategic intervals along the flight path, critical parameters such as distance, altitude profiles, and flight path angles are dynamically assessed. Additionally, edge computing techniques enhance data processing speeds, ensuring adaptability to various scenarios. This paper challenges existing paradigms in flight management and opens avenues for further research in integrating edge computing within aviation technology. The findings presented herein carry significant implications for the aviation industry, ranging from improved operational efficiency to heightened safety measures.