• 한국안전학회지
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• 제33권6호
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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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• 제43권3호
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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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• 제53권5호
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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.

• 정보교육학회논문지
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• 제26권5호
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• pp.295-305
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• 2022

현대 인공지능사회에 있어서 교육의 목표는 인공지능사회에 잘 적응하는 동시에 변화를 선도할 수 있는 융합형 인재를 양성하는 것이다. 융합형 미래인재에게 요구되는 핵심역량은 소프트웨어를 잘 이해하고 더 나아가 생성해낼 수 있는 것이다. 이러한 측면에서 알고리즘 교육은 매우 중요하다. 이에 본 연구에서는 융합적 사고력 향상을 위한 알고리즘 교육 프로그램을 개발하였다. 또한, 융합적 사고력 사전-사후 검사를 실시하여 대응표본 t-test를 통한 효과성을 검증한 결과, 학생들의 알고리즘 지식, 기능의 영역에서 융합적 사고력이 유의하게 향상된 것으로 나타났다. 본 연구 결과는 향후 알고리즘 교육에 유용하게 사용될 것으로 기대한다.

• 대한수학회보
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• 제60권3호
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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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• 제49권4호
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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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• 제17권11호
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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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• 제11권1호
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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.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.1959-1974
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• 2024

Nuclear energy plays a crucial role in energy supply in the 21st century, and more and more Nuclear Power Plants (NPPs) will be in operation to contribute to the development of human society. However, as a typical complex system engineering, the operation and development of NPPs require efficient and stable control methods to ensure the safety and efficiency of nuclear power generation. Reinforcement learning (RL) aims at learning optimal control policies via maximizing discounted long-term rewards. The reward-oriented learning paradigm has witnessed remarkable success in many complex systems, such as wind power systems, electric power systems, coal fire power plants, robotics, etc. In this work, we try to present a systematic review of the applications of RL on these complex systems, from which we believe NPPs can borrow experience and insights. We then conduct a block-by-block investigation on the application scenarios of specific tasks in NPPs and carried out algorithmic research for different situations such as power startup, collaborative control, and emergency handling. Moreover, we discuss the possibilities of further application of RL methods on NPPs and detail the challenges when applying RL methods on NPPs. We hope this work can boost the realization of intelligent NPPs, and contribute to more and more research on how to better integrate RL algorithms into NPPs.

• 전기전자학회논문지
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• 제13권1호
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• pp.77-86
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• 2009

본 논문에서 이식형 심장 박동 조율기를 위한 심전도 검출기와 아날로그-디지털 변환기(ADC)를 설계한다. 제안한 웨이블렛 심전도 검출기는 웨이블렛 필터 뱅크 구조의 웨이블렛 변조기, 웨이블렛 합성된 심전도 신호의 가설 검정을 통한 QRS 신호 검출기와 0-교차점을 이용한 잡음 검출기로 구성된다. 저전력 소모의 동작을 유지하며 보다 높은 검출 정확도를 갖는 심전도 검출기의 구현을 위해, 다중스케일 곱의 알고리즘과 적응형의 임계값을 갖는 알고리즘을 사용하였다. 또한 심전도 검출기의 입력단에 위치하는 저전력 Successive Approximation Register ADC의 구현을 위해, 신호 변환의 주기 중, 매우 짧은 시간 동안에만 동작하는 비교기와 수동 소자로 구성되는 Sample&Hold를 사용하였다. 제안한 회로는 표준 CMOS $0.35{\mu}m$ 공정을 사용하여 집적 및 제작되었고, 99.32%의 높은 검출 정확도와 3V의 전원 전압에서 $19.02{\mu}W$의 매우 낮은 전력 소모를 갖는 것을 실험을 통해 확인하였다.