Acoustic emission (AE) is one of the most relevant and cost-effective non-destructive testing methods for identifying defects in industrial objects. This paper addresses two primary tasks in defectoscopy: locating haz...
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ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
Acoustic emission (AE) is one of the most relevant and cost-effective non-destructive testing methods for identifying defects in industrial objects. This paper addresses two primary tasks in defectoscopy: locating hazardous AE zones and classifying signals according to the presumed defect type. Through our research, we have determined and experimentally confirmed that the most suitable machine learning method for localization is a modified DBSCAN, while Agglomerative Clustering is optimal for typification. This paper details the specific tuning and modifications applied to these methods for AE. The resulting models were successfully tested on real-world data. The accuracy of the localization results was assessed using statistical analysis and consultations with AE experts, while typification was validated probabilistically and tested on sample data.
1 Introduction Traditional covert communication[1]usually relies on centralized channels,which are efficient in transmission but susceptible to eavesdropping and attacks,leading to information ***,distributed methods ...
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1 Introduction Traditional covert communication[1]usually relies on centralized channels,which are efficient in transmission but susceptible to eavesdropping and attacks,leading to information ***,distributed methods also exist,such as peer-to-peer networks and distributed hash *** technology,which is decentralized,anonymous,non-tamperable and anti-attack,can be applied to covert communication to solve the problems of traditional methods and improve the quality of communication.
Concepts and ideas are presented that should characterize an advanced Ada programming support environment. These concepts include: 1. conceptual simplicity, 2. tight coupling of tools, and 3. effective support of host...
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Concepts and ideas are presented that should characterize an advanced Ada programming support environment. These concepts include: 1. conceptual simplicity, 2. tight coupling of tools, and 3. effective support of host-target software development. Several important principles - including template-assisted Ada editing, command completion employing Ada as a command language, and combining the advantages of interpretation and compilation - have been demonstrated in the Arcturus system. Of particular concern is the expansion of Arcturus to support the analysis, testing, and debugging of real-time tasking programs. A strategy is proposed for applying the individual standalone techniques developed to facilitate those activities in an integrated manner, making them even more valuable as they are mutually reinforcing and complementary in vital ways. The strategy provides a disciplined approach to the problem of analyzing errors disclosed during real-time target machine testing.
Since the mid-1970s, and with the development of each new programming paradigm there has been an increasing interest in exceptions and the benefits of exception handling. With the move towards programming for ever mor...
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Since the mid-1970s, and with the development of each new programming paradigm there has been an increasing interest in exceptions and the benefits of exception handling. With the move towards programming for ever more complex architectures, understanding basic facilities such as exception handling as an aid to improving program reliability, robustness and comprehensibility has become much more important. Interest has sparked the production of many papers both theoretical and practical, each giving a view of exceptions and exception handling from a different standpoint. In an effort to provide a means of classifying exception handling models which may be encountered, a taxonomy is presented in this paper. As the taxonomy is developed some of the concepts of exception handling are introduced and discussed. The taxonomy is applied to a number of exception handling models in some contemporary programming languages and some observations and conclusions offered.
The problem of automatic explanation of reasoning, especially as it relates to expert systems is examined. Explanation means the ability of a program to discuss what it is doing in some understandable way. A general f...
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The problem of automatic explanation of reasoning, especially as it relates to expert systems is examined. Explanation means the ability of a program to discuss what it is doing in some understandable way. A general framework in which to view explanation and review some of the research done in this area is presented. The explanation system for NEOMYCIN, a medical consultation program is studied. A consultation program interactively helps a user to solve a problem. Our goal is to have NEOMYCIN explain its problem-solving strategies. An explanation of strategy describes the plan the program is using to reach a solution. Such an explanation is usually concrete, referring to aspects of the current problem situation. Abstract explanations articulate a general principle, which can be applied in different situations;such explanations are useful in teaching and in explaining by analogy. The aspects of NEOMYCIN that make abstract strategic explanations possible, the representation of strategic knowledge explicitly and separately from domain knowledge and demonstrate how this representation can be used to generate explanations, is described.
Fail-safety is a system attribute which ensures that a program either completes its execution satisfying its post-conditions in the normal manner or signals its failure to do so to its operating environment. Such an a...
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Fail-safety is a system attribute which ensures that a program either completes its execution satisfying its post-conditions in the normal manner or signals its failure to do so to its operating environment. Such an attribute is desirable of any system as it ensures the correctness of results which are produced. A very few modern sequential programming languages offer program fail-safety through the judicious use of a well designed exception handling mechanism. In this paper the exception handling techniques used in those sequential systems are developed to provide the guidelines for fail-safe concurrent system design. (C) 1997 Elsevier science Ltd.
In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious t...
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In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to c
Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, mycin, indicates that the represen...
Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, mycin, indicates that the representation has serious limitations: people other than the original rule authors find it difficult to modify the rule set, and the rules are unsuitable for use in other settings, such as for application to teaching. These problems are rooted in fundamental limitations in mycin"s original rule representation: the view that expert knowledge can be encoded as a uniform, weakly structured set of if/then associations is found to be wanting. To illustrate these problems, this paper examines mycin"s rules from the perspective of a teacher trying to justify them and to convey a problem-solving approach. We discover that individual rules play different roles, have different kinds of justifications, and are constructed using different rationales for the ordering and choice of premise clauses. This design knowledge, consisting of structural and strategic concepts which lie outside the representation, is shown to be procedurally embedded in the rules. Moreover, because the data/hypothesis associations are themselves a proceduralized form of underlying disease models, they can only be supported by appealing to this deeper level of knowledge. Making explicit this structural, strategic and support knowledge enhances the ability to understand and modify the system.
This paper presents an extension of our Mathematica- and MathCode-based symbolic-numeric framework for solving a variety of partial differential equation (PDE) problems. The main features of our earlier work, which im...
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This paper presents an extension of our Mathematica- and MathCode-based symbolic-numeric framework for solving a variety of partial differential equation (PDE) problems. The main features of our earlier work, which implemented explicit finite-difference schemes, include the ability to handle (1) arbitrary number of dependent variables, (2) arbitrary dimensionality, and (3) arbitrary geometry, as well as (4) developing finite-difference schemes to any desired order of approximation. In the present paper, extensions of this framework to implicit schemes and the method of lines are discussed. While C++ code is generated, using the MathCode system for the implicit method, Modelica code is generated for the method of lines. The latter provides a preliminary PDE support for the Modelica language. Examples illustrating the various aspects of the solver generator are presented.
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