Hybrid Wavelet-Fourier-HMM Speaker Recognition

Bartosz Zio lko,Wojciech Koz lowski,Mariusz Zio lko,Rafa l Samborski,David Sierra,Jakub Ga lka 보안공학연구지원센터 2011 International Journal of Hybrid Information Techno Vol.4 No.4

MODELLING THE DYNAMICS OF THE LEAD BISMUTH EUTECTIC EXPERIMENTAL ACCELERATOR DRIVEN SYSTEM BY AN INFINITE IMPULSE RESPONSE LOCALLY RECURRENT NEURAL NETWORK

Zio, Enrico,Pedroni, Nicola,Broggi, Matteo,Golea, Lucia Roxana Korean Nuclear Society 2009 Nuclear Engineering and Technology Vol.41 No.10

In this paper, an infinite impulse response locally recurrent neural network (IIR-LRNN) is employed for modelling the dynamics of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The network is trained by recursive back-propagation (RBP) and its ability in estimating transients is tested under various conditions. The results demonstrate the robustness of the locally recurrent scheme in the reconstruction of complex nonlinear dynamic relationships.

RISK-INFORMED REGULATION: HANDLING UNCERTAINTY FOR A RATIONAL MANAGEMENT OF SAFETY

Zio, Enrico Korean Nuclear Society 2008 Nuclear Engineering and Technology Vol.40 No.5

A risk-informed regulatory approach implies that risk insights be used as supplement of deterministic information for safety decision-making purposes. In this view, the use of risk assessment techniques is expected to lead to improved safety and a more rational allocation of the limited resources available. On the other hand, it is recognized that uncertainties affect both the deterministic safety analyses and the risk assessments. In order for the risk-informed decision making process to be effective, the adequate representation and treatment of such uncertainties is mandatory. In this paper, the risk-informed regulatory framework is considered under the focus of the uncertainty issue. Traditionally, probability theory has provided the language and mathematics for the representation and treatment of uncertainty. More recently, other mathematical structures have been introduced. In particular, the Dempster-Shafer theory of evidence is here illustrated as a generalized framework encompassing probability theory and possibility theory. The special case of probability theory is only addressed as term of comparison, given that it is a well known subject. On the other hand, the special case of possibility theory is amply illustrated. An example of the combination of probability and possibility for treating the uncertainty in the parameters of an event tree is illustrated.

OPTIMIZATION OF THE TEST INTERVALS OF A NUCLEAR SAFETY SYSTEM BY GENETIC ALGORITHMS, SOLUTION CLUSTERING AND FUZZY PREFERENCE ASSIGNMENT

Zio, E.,Bazzo, R. Korean Nuclear Society 2010 Nuclear Engineering and Technology Vol.42 No.4

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

RISK-INFORMED REGULATION: HANDLING UNCERTAINTY FOR A RATIONAL MANAGEMENT OF SAFETY

ENRICO ZIO 한국원자력학회 2008 Nuclear Engineering and Technology Vol.40 No.5

A risk-informed regulatory approach implies that risk insights be used as supplement of deterministic information for safety decision-making purposes. In this view, the use of risk assessment techniques is expected to lead to improved safety and a more rational allocation of the limited resources available. On the other hand, it is recognized that uncertainties affect both the deterministic safety analyses and the risk assessments. In order for the risk-informed decision making process to be effective, the adequate representation and treatment of such uncertainties is mandatory. In this paper, the risk-informed regulatory framework is considered under the focus of the uncertainty issue. Traditionally, probability theory has provided the language and mathematics for the representation and treatment of uncertainty. More recently, other mathematical structures have been introduced. In particular, the Dempster-Shafer theory of evidence is here illustrated as a generalized framework encompassing probability theory and possibility theory. The special case of probability theory is only addressed as term of comparison, given that it is a well known subject. On the other hand, the special case of possibility theory is amply illustrated. An example of the combination of probability and possibility for treating the uncertainty in the parameters of an event tree is illustrated.

OPTIMIZATION OF THE TEST INTERVALS OF A NUCLEAR SAFETY SYSTEM BY GENETIC ALGORITHMS, SOLUTION CLUSTERING AND FUZZY PREFERENCE ASSIGNMENT CLUSTERING AND FUZZY PREFERENCE ASSIGNMENT

E. ZIO,R.BAZZO 한국원자력학회 2010 Nuclear Engineering and Technology Vol.42 No.4

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into “families”. On the basis of the decision maker’s preferences, each family is then synthetically represented by a “head of the family” solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

MODELLING THE DYNAMICS OF THE LEAD BISMUTH EUTECTIC EXPERIMENTAL ACCELERATOR DRIVEN SYSTEM BY AN INFINITE IMPULSE RESPONSE LOCALLY RECURRENT NEURAL NETWORK

ENRICO ZIO,NICOLA PEDRONI,MATTEO BROGGI,LUCIA ROXANA GOLEA 한국원자력학회 2009 Nuclear Engineering and Technology Vol.41 No.10

In this paper, an infinite impulse response locally recurrent neural network (IIR-LRNN) is employed for modelling the dynamics of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The network is trained by recursive back-propagation (RBP) and its ability in estimating transients is tested under various conditions. The results demonstrate the robustness of the locally recurrent scheme in the reconstruction of complex nonlinear dynamic relationships.

『合部金光明經』 字吐口訣의 誤記

김지오(Kim Zio) 구결학회 2010 구결연구 Vol.25 No.-

『合部金光明經』의 字吐口訣에는 다른 자료들에서 보기 힘든 誤ㆍ脫字들이 많이 나타나므로 이 자료의 口訣을 올바로 사용하기 위해서는 誤記에 대한 이해가 반드시 선행되어야 한다. 『合部金光明經』에 나타난 誤記의 유형은 逆讀點오류, 懸吐 위치 오류, 吐 漏落, 잘못된 口訣 懸吐로 구분할 수 있는데, 이 중 상당 부분이 지우기, 덧쓰기, 부호 등을 통해 修訂되어 있다. 또한 『合部金光明經』에 나타난 唯一例(稀貴例)의 상당 부분이 誤記일 가능성이 있으므로 『合部金光明經』의 稀貴例를 다룰 때에는 신중을 기해야 한다. 『Hapbugeumgwangmyeonggyeong』 kugyol has many errors in print compared to the existing seokdokkugyol documents. It is critical to understand misprints frequently found in 『Hapbugeumgwangmyeonggyeong』 to quote the resource. Generally, the errors can be divided into four types; yeogdogjeom(逆讀點) error, adding To(吐) into a wrong place, missing out To(吐), and using wrong To(吐). It is noticeable that most of these were found to be revised by erasing, rewriting and marking. It is believed that a commentator made such mistakes without a full understanding of contexts when copying a original text. Finally, since there is a quiet possibility that most rare nouns(稀貴語) found in 『Hapbugeumgwangmyeonggyeong』 could be misprinted, it is careful to handle them. Also, as it is more often to find the enumerated misprints from 7, 13, 14 and 15 page, it requires a special attention to make use of the resources.

Risk-informed approach to the safety improvement of the reactor protection system of the AGN-201K research reactor

Ahmed, Ibrahim,Zio, Enrico,Heo, Gyunyoung Korean Nuclear Society 2020 Nuclear Engineering and Technology Vol.52 No.4

Periodic safety reviews (PSRs) are conducted on operating nuclear power plants (NPPs) and have been mandated also for research reactors in Korea, in response to the Fukushima accident. One safety review tool, the probabilistic safety assessment (PSA), aims to identify weaknesses in the design and operation of the research reactor, and to evaluate and compare possible safety improvements. However, the PSA for research reactors is difficult due to scarce data availability. An important element in the analysis of research reactors is the reactor protection system (RPS), with its functionality and importance. In this view, we consider that of the AGN-201K, a zero-power reactor without forced decay heat removal systems, to demonstrate a risk-informed safety improvement study. By incorporating risk- and safety-significance importance measures, and sensitivity and uncertainty analyses, the proposed method identifies critical components in the RPS reliability model, systematically proposes potential safety improvements and ranks them to assist in the decision-making process.

Physics informed neural networks for surrogate modeling of accidental scenarios in nuclear power plants

Antonello Federico,Buongiorno Jacopo,Zio Enrico 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.9

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity ‘black-box’ models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results