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Classification of board defects in semiconductor manufacturing
Jašek, Filip ; Vágner, Martin (referee) ; Dřínovský, Jiří (advisor)
This diploma thesis focuses on detecting defects in semiconductor wafer manufacturing. It explores methods for identifying faulty chips and controlling yield during production. To classify defects machine learning techniques are used. Initially, ResNet18 architecture was used for inference, but low accuracy was attributed to limited input data. Transfer learning with ResNet50v2 was then attempted, resulting in improved metric with different dataset. Hyperparameter tuning and data augmentations were also explored. The study found that autoencoders for data compression during inference increased speed but led to degraded evaluation metrics.
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Component Interconnection Inference
Olšarová, Nela ; Rychlý, Marek (referee) ; Křivka, Zbyněk (advisor)
The Master Thesis deals with the design of hardware component interconnection inference algorithm that is supposed to be used in the FPGA schema editor that was integrated into educational integrated development environment VLAM IDE. The aim of the algorithm is to support user by finding an optimal interconnection of two given components. The editor and the development environment are implemented as an Eclipse plugin using GMF framework. A brief description of this technologies and the embedded systems design are followed by the design of the inference algorithm. This problem is a topic of combinatorial optimization, related to the bipartite matching and assignment problem. After this, the implementation of the algorithm is described, followed by tests and a summary of achieved results.
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Bayesian Networks Applications
Chaloupka, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This master's thesis deals with possible applications of Bayesian networks. The theoretical part is mainly of mathematical nature. At first, we focus on general probability theory and later we move on to the theory of Bayesian networks and discuss approaches to inference and to model learning while providing explanations of pros and cons of these techniques. The practical part focuses on applications that demand learning a Bayesian network, both in terms of network parameters as well as structure. These applications include general benchmarks, usage of Bayesian networks for knowledge discovery regarding the causes of criminality and exploration of the possibility of using a Bayesian network as a spam filter.
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Problem diagnostics by expert system
Mertlík, Tomáš ; Nagy, Ľuboš (referee) ; Karásek, Jan (advisor)
This paper refers to expert systems. Expert system is a computer program dedicated to giving an expert advice, decisions or recommending the best solution in a particular situation. This paper mentions four types of expert system which are commonly used in practice. Basic, additional and complementary components of expert systems are described. The practical part of this paper refers to the diagnostic problem of computer start based on expert system implemented in JAVA programming language and ECLIPSE IDE.
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Diagnostic expert system
Mertlík, Tomáš ; Nagy, Ľuboš (referee) ; Karásek, Jan (advisor)
This paper refers to expert systems. Expert system is a computer program dedicated to giving an expert advice, decisions or recommending the best solution in a particular situation. This paper mentions four types of expert system which are commonly used in practice. Basic, additional and complementary components of expert systems are described. The practical part of this paper refers to the diagnostic problem of computer start based on expert system implemented in JAVA programming language and ECLIPSE IDE.
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Classification of board defects in semiconductor manufacturing
Jašek, Filip ; Vágner, Martin (referee) ; Dřínovský, Jiří (advisor)
This diploma thesis focuses on detecting defects in semiconductor wafer manufacturing. It explores methods for identifying faulty chips and controlling yield during production. To classify defects machine learning techniques are used. Initially, ResNet18 architecture was used for inference, but low accuracy was attributed to limited input data. Transfer learning with ResNet50v2 was then attempted, resulting in improved metric with different dataset. Hyperparameter tuning and data augmentations were also explored. The study found that autoencoders for data compression during inference increased speed but led to degraded evaluation metrics.
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Classification of board defects in semiconductor manufacturing
Jašek, Filip ; Vágner, Martin (referee) ; Dřínovský, Jiří (advisor)
This diploma thesis focuses on detecting defects in semiconductor wafer manufacturing. It explores methods for identifying faulty chips and controlling yield during production. To classify defects machine learning techniques are used. Initially, ResNet18 architecture was used for inference, but low accuracy was attributed to limited input data. Transfer learning with ResNet50v2 was then attempted, resulting in improved metric with different dataset. Hyperparameter tuning and data augmentations were also explored. The study found that autoencoders for data compression during inference increased speed but led to degraded evaluation metrics.
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Fallibilism and Semiotics of Charles Sanders Peirce
Macháček, Martin ; Karľa, Michal (advisor) ; Švantner, Martin (referee)
This thesis consists of the analysis of Peirce's essays Questions Concering Certain Faculties Claimed for Man and Some Consequences of Four Incapacities focused on the genesis of fallibilism and its dependence on the theory of representation. Peirce's epistemological position here is articulated as a rejection of foundationalism and its conditions (e.g. intuition and introspection) that are understood to be unfounded hypotheses due the character of our knowledge of the outside world. The aim of this thesis is to find out how Peirce's epistemology can work without the certainty of foundationalism. Keywords: Peirce, fallibilism, representation, critique of foundationalism, inference, epistemology
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Inference of an XML Schema with the Knowledge of XML Operations
Mikula, Mário ; Holubová, Irena (advisor) ; Svoboda, Martin (referee)
Recently, plenty of methods dealing with automatic inference of XML schema have been developed, however, most of them utilize XML documents as their only input. In this thesis we focus on extending inference by incorporating XML operations, in particular XQuery queries. We discuss how can XQuery queries help in improving the inference process and we propose an algorithm based on chosen improvements, extending an existing method of a key discovery, that can be integrated to methods inferring so-called initial grammar. By implementing it, we created the first solution of XML schema inference using XML documents along with XML operations.
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Inference of XML Integrity Constraints
Vitásek, Matej ; Holubová, Irena (advisor) ; Knap, Tomáš (referee)
In this work we expand upon the previous efforts to infer schema information from existing XML documents. We find the inference of structure to be sufficiently researched and focus further on integrity constraints. After briefly introducing some of them we turn our attention to ID/IDREF/IDREFS attributes in DTD. Building on the research by Barbosa and Menelzon (2003) we introduce a heuristic approach to the problem of finding an optimal ID set. The approach is evaluated and tuned in a wide range of experiments.
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