Relevant bibliographies by topics / Operator Learning

Academic literature on the topic 'Operator Learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Operator Learning"

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Zhang, Pinggai, Ling Wang, Jiaojie Du, Zixiang Fei, Song Ye, Minrui Fei, and Panos M. Pardalos. "Differential Human Learning Optimization Algorithm." Computational Intelligence and Neuroscience 2022 (April 30, 2022): 1–19. http://dx.doi.org/10.1155/2022/5699472.

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Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.
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Andreiana, Doru Stefan, Luis Enrique Acevedo Galicia, Seppo Ollila, Carlos Leyva Guerrero, Álvaro Ojeda Roldán, Fernando Dorado Navas, and Alejandro del Real Torres. "Steelmaking Process Optimised through a Decision Support System Aided by Self-Learning Machine Learning." Processes 10, no. 3 (February 22, 2022): 434. http://dx.doi.org/10.3390/pr10030434.

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This paper presents the application of a reinforcement learning (RL) algorithm, concretely Q-Learning, as the core of a decision support system (DSS) for a steelmaking subprocess, the Composition Adjustment by Sealed Argon-bubbling with Oxygen Blowing (CAS-OB) from the SSAB Raahe steel plant. Since many CAS-OB actions are selected based on operator experience, this research aims to develop a DSS to assist the operator in taking the proper decisions during the process, especially less experienced operators. The DSS is intended to supports the operators in real-time during the process to facilitate their work and optimise the process, improving material and energy efficiency, thus increasing the operation’s sustainability. The objective is that the algorithm learns the process based only on raw data from the CAS-OB historical database, and on rewards set according to the objectives. Finally, the DSS was tested and validated by a developer engineer from the CAS-OB steelmaking plant. The results show that the algorithm successfully learns the process, recommending the same actions as those taken by the operator 69.23% of the time. The algorithm also suggests a better option in 30.76% of the remaining cases. Thanks to the DSS, the heat rejection due to wrong composition is reduced by 4%, and temperature accuracy is increased to 83.33%. These improvements resulted in an estimated reduction of 2% in CO2 emissions, 0.5% in energy consumption and 1.5% in costs. Additionally, actions taken based on the operator’s experience are incorporated into the DSS knowledge, facilitating the integration of operators with lower experience in the process.
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Teğin, Uğur, Mustafa Yıldırım, İlker Oğuz, Christophe Moser, and Demetri Psaltis. "Scalable optical learning operator." Nature Computational Science 1, no. 8 (August 2021): 542–49. http://dx.doi.org/10.1038/s43588-021-00112-0.

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Dubey, Akash Dutt, and Ravi Bhushan Mishra. "Cognition of a Robotic Manipulator Using the Q-Learning Based Situation-Operator Model." Journal of Information Technology Research 11, no. 1 (January 2018): 146–57. http://dx.doi.org/10.4018/jitr.2018010109.

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In this article, we have applied cognition on robot using Q-learning based situation operator model. The situation operator model takes the initial situation of the mobile robot and applies a set of operators in order to move the robot to the destination. The initial situation of the mobile robot is defined by a set of characteristics inferred by the sensor inputs. The Situation-Operator Model (SOM) model comprises of a planning and learning module which uses certain heuristics for learning through the mobile robot and a knowledge base which stored the experiences of the mobile robot. The control and learning of the robot is done using q-learning. A camera sensor and an ultrasonic sensor were used as the sensory inputs for the mobile robot. These sensory inputs are used to define the initial situation, which is then used in the learning module to apply the valid operator. The results obtained by the proposed method were compared to the result obtained by Reinforcement-Based Artificial Neural Network for path planning.
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Li-Chao Feng, Li-Chao Feng, Xing-Ya Wang Li-Chao Feng, Shi-Yu Zhang Xing-Ya Wang, Rui-Zhi Gao Shi-Yu Zhang, and Zhi-Hong Zhao Rui-Zhi Gao. "Mutation Operator Reduction for Cost-effective Deep Learning Software Testing via Decision Boundary Change Measurement." 網際網路技術學刊 23, no. 3 (May 2022): 601–10. http://dx.doi.org/10.53106/160792642022052303018.

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<p>Mutation testing has been deemed an effective way to ensure Deep Learning (DL) software quality. Due to the requirements of generating and executing mass mutants, mutation testing suffers low-efficiency problems. In regard to traditional software, mutation operators that are hard to cause program logic changes can be reduced. Thus, the number of the mutants, as well as their executions, can be effectively decreased. However, DL software relies on model logic to make a decision. Decision boundaries characterize its logic. In this paper, we propose a DL software mutation operator reduction technique. Specifically, for each group of DL operators, we propose and use DocEntropy to measure the model&rsquo;s decision boundary changes among mutants generated and the original model. Then, we select the operator group with the highest entropy value and use the involved operators for further mutation testing. An empirical study on two DL models verified that the proposed approach could lead to cost-effective DL software mutation testing (i.e., 33.61% mutants and their executions decreased on average) and archive more accuracy mutation scores (i.e., 9.45% accuracy increased on average).</p> <p>&nbsp;</p>
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Wei, Changyun, Fusheng Ni, and Xiujing Chen. "Obtaining Human Experience for Intelligent Dredger Control: A Reinforcement Learning Approach." Applied Sciences 9, no. 9 (April 28, 2019): 1769. http://dx.doi.org/10.3390/app9091769.

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This work presents a reinforcement learning approach for intelligent decision-making of a Cutter Suction Dredger (CSD), which is a special type of vessel for deepening harbors, constructing ports or navigational channels, and reclaiming landfills. Currently, CSDs are usually controlled by human operators, and the production rate is mainly determined by the so-called cutting process (i.e., cutting the underwater soil into fragments). Long-term manual operation is likely to cause driving fatigue, resulting in operational accidents and inefficiencies. To reduce the labor intensity of the operator, we seek an intelligent controller the can manipulate the cutting process to replace human operators. To this end, our proposed reinforcement learning approach consists of two parts. In the first part, we employ a neural network model to construct a virtual environment based on the historical dredging data. In the second part, we develop a reinforcement learning model that can lean the optimal control policy by interacting with the virtual environment to obtain human experience. The results show that the proposed learning approach can successfully imitate the dredging behavior of an experienced human operator. Moreover, the learning approach can outperform the operator in a way that can make quick responses to the change in uncertain environments.
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Kurdel, Pavol, František Adamčík, and Ján Labun. "Adequacy of Estimation Model of Asymptotic Learning Operator – Pilot Function." Naše more 62, SI (October 2015): 224–27. http://dx.doi.org/10.17818/nm/2015/si25.

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Hwang, Rakhoon, Jae Yong Lee, Jin Young Shin, and Hyung Ju Hwang. "Solving PDE-Constrained Control Problems Using Operator Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4504–12. http://dx.doi.org/10.1609/aaai.v36i4.20373.

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The modeling and control of complex physical systems are essential in real-world problems. We propose a novel framework that is generally applicable to solving PDE-constrained optimal control problems by introducing surrogate models for PDE solution operators with special regularizers. The procedure of the proposed framework is divided into two phases: solution operator learning for PDE constraints (Phase 1) and searching for optimal control (Phase 2). Once the surrogate model is trained in Phase 1, the optimal control can be inferred in Phase 2 without intensive computations. Our framework can be applied to both data-driven and data-free cases. We demonstrate the successful application of our method to various optimal control problems for different control variables with diverse PDE constraints from the Poisson equation to Burgers' equation.
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Yuniarto, Yuniarto, and Jann Hidayat Tjakraatmadja. "Toward learning organization in a telecom operator network operation center." International Journal of ADVANCED AND APPLIED SCIENCES 4, no. 11 (November 2017): 148–54. http://dx.doi.org/10.21833/ijaas.2017.011.024.

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Hirata, Nina S. T., and George A. Papakostas. "On Machine-Learning Morphological Image Operators." Mathematics 9, no. 16 (August 5, 2021): 1854. http://dx.doi.org/10.3390/math9161854.

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Morphological operators are nonlinear transformations commonly used in image processing. Their theoretical foundation is based on lattice theory, and it is a well-known result that a large class of image operators can be expressed in terms of two basic ones, the erosions and the dilations. In practice, useful operators can be built by combining these two operators, and the new operators can be further combined to implement more complex transformations. The possibility of implementing a compact combination that performs a complex transformation of images is particularly appealing in resource-constrained hardware scenarios. However, finding a proper combination may require a considerable trial-and-error effort. This difficulty has motivated the development of machine-learning-based approaches for designing morphological image operators. In this work, we present an overview of this topic, divided in three parts. First, we review and discuss the representation structure of morphological image operators. Then we address the problem of learning morphological image operators from data, and how representation manifests in the formulation of this problem as well as in the learned operators. In the last part we focus on recent morphological image operator learning methods that take advantage of deep-learning frameworks. We close with discussions and a list of prospective future research directions.
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Dissertations / Theses on the topic "Operator Learning"

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Tummaluri, Raghuram R. "Operator Assignment in Labor Intensive Cells Considering Operation Time Based Skill Levels, Learning and Forgetting." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1126900571.

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Kienzle, Wolf. "Learning an interest operator from human eye movements." Berlin Logos-Verl, 2008. http://d-nb.info/990541908/04.

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Schrödl, Stefan J. "Operator valued reproducing kernels and their application in approximation and statistical learning." Aachen Shaker, 2009. http://d-nb.info/99654559X/04.

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Huusari, Riikka. "Kernel learning for structured data : a study on learning operator - and scalar - valued kernels for multi-view and multi-task learning problems." Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0312.

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Aujourd'hui il y a plus en plus des données ayant des structures non-standard. Cela inclut le cadre multi-tâches où chaque échantillon de données est associé à plusieurs étiquettes de sortie, ainsi que le paradigme d'apprentissage multi-vues, dans lequel chaque échantillon de données a de nombreuses descriptions. Il est important de bien modéliser les interactions présentes dans les vues ou les variables de sortie.Les méthodes à noyaux offrent un moyen justifié et élégant de résoudre de problèmes d’apprentissage. Les noyaux à valeurs opérateurs, qui généralisent les noyaux à valeur scalaires, ont récemment fait l’objet d’une attention. Toujours le choix d’une fonction noyau adaptée aux données joue un rôle crucial dans la réussite de la tâche d’apprentissage.Cette thèse propose l’apprentissage des noyaux comme une solution à problèmes d’apprentissage automatique de multi-tâches et multi-vues. Les chapitres deux et trois étudient l’apprentissage des interactions entre données à vues multiples. Le deuxième chapitre considère l'apprentissage inductif supervisé et les interactions sont modélisées avec des noyaux à valeurs opérateurs. Le chapitre trois traite un contexte non supervisé et propose une méthode d’apprentissage du noyau à valeurs scalaires pour compléter les données manquantes dans les matrices à noyaux issues d’un problème à vues multiples. Dans le dernier chapitre, nous passons à un apprentissage à sorties multiples, pour revenir au paradigme de l'apprentissage inductif supervisé. Nous proposons une méthode d’apprentissage de noyaux inséparables à valeurs opérateurs qui modélisent les interactions entre les entrées et de multiples variables de sortie
Nowadays datasets with non-standard structures are more and more common. Examples include the already well-known multi-task framework where each data sample is associated with multiple output labels, as well as the multi-view learning paradigm, in which each data sample can be seen to contain numerous descriptions. To obtain a good performance in tasks like these, it is important to model the interactions present in the views or output variables well.Kernel methods offer a justified and elegant way to solve many machine learning problems. Operator-valued kernels, which generalize the well-known scalar-valued kernels, have gained attention recently as a way to learn vector-valued functions. The choice of a good kernel function plays crucial role for the success on the learning task.This thesis offers kernel learning as a solution for various machine learning problems. Chapters two and three investigate learning the data interactions with multi-view data. In the first of these, the focus is in supervised inductive learning and the interactions are modeled with operator-valued kernels. Chapter three tackles multi-view data and kernel learning in unsupervised context and proposes a scalar-valued kernel learning method for completing missing data in kernel matrices of a multi-view problem. In the last chapter we turn from multi-view to multi-output learning, and return to the supervised inductive learning paradigm. We propose a method for learning inseparable operator-valued kernels that model interactions between inputs and multiple output variables
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Montagner, Igor dos Santos. "W-operator learning using linear models for both gray-level and binary inputs." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-21082017-111455/.

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Image Processing techniques can be used to solve a broad range of problems, such as medical imaging, document processing and object segmentation. Image operators are usually built by combining basic image operators and tuning their parameters. This requires both experience in Image Processing and trial-and-error to get the best combination of parameters. An alternative approach to design image operators is to estimate them from pairs of training images containing examples of the expected input and their processed versions. By restricting the learned operators to those that are translation invariant and locally defined ($W$-operators) we can apply Machine Learning techniques to estimate image transformations. The shape that defines which neighbors are used is called a window. $W$-operators trained with large windows usually overfit due to the lack sufficient of training data. This issue is even more present when training operators with gray-level inputs. Although approaches such as the two-level design, which combines multiple operators trained on smaller windows, partly mitigates these problems, they also require more complicated parameter determination to achieve good results. In this work we present techniques that increase the window sizes we can use and decrease the number of manually defined parameters in $W$-operator learning. The first one, KA, is based on Support Vector Machines and employs kernel approximations to estimate image transformations. We also present adequate kernels for processing binary and gray-level images. The second technique, NILC, automatically finds small subsets of operators that can be successfully combined using the two-level approach. Both methods achieve competitive results with methods from the literature in two different application domains. The first one is a binary document processing problem common in Optical Music Recognition, while the second is a segmentation problem in gray-level images. The same techniques were applied without modification in both domains.
Processamento de imagens pode ser usado para resolver problemas em diversas áreas, como imagens médicas, processamento de documentos e segmentação de objetos. Operadores de imagens normalmente são construídos combinando diversos operadores elementares e ajustando seus parâmetros. Uma abordagem alternativa é a estimação de operadores de imagens a partir de pares de exemplos contendo uma imagem de entrada e o resultado esperado. Restringindo os operadores considerados para o que são invariantes à translação e localmente definidos ($W$-operadores), podemos aplicar técnicas de Aprendizagem de Máquina para estimá-los. O formato que define quais vizinhos são usadas é chamado de janela. $W$-operadores treinados com janelas grandes frequentemente tem problemas de generalização, pois necessitam de grandes conjuntos de treinamento. Este problema é ainda mais grave ao treinar operadores em níveis de cinza. Apesar de técnicas como o projeto dois níveis, que combina a saída de diversos operadores treinados com janelas menores, mitigar em parte estes problemas, uma determinação de parâmetros complexa é necessária. Neste trabalho apresentamos duas técnicas que permitem o treinamento de operadores usando janelas grandes. A primeira, KA, é baseada em Máquinas de Suporte Vetorial (SVM) e utiliza técnicas de aproximação de kernels para realizar o treinamento de $W$-operadores. Uma escolha adequada de kernels permite o treinamento de operadores níveis de cinza e binários. A segunda técnica, NILC, permite a criação automática de combinações de operadores de imagens. Este método utiliza uma técnica de otimização específica para casos em que o número de características é muito grande. Ambos métodos obtiveram resultados competitivos com algoritmos da literatura em dois domínio de aplicação diferentes. O primeiro, Staff Removal, é um processamento de documentos binários frequente em sistemas de reconhecimento ótico de partituras. O segundo é um problema de segmentação de vasos sanguíneos em imagens em níveis de cinza.
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Alhawari, Omar I. "Operator Assignment Decisions in a Highly Dynamic Cellular Environment." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1221596218.

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Schrödl, Stefan J. [Verfasser]. "Operator-valued Reproducing Kernels and Their Application in Approximation and Statistical Learning / Stefan J Schrödl." Aachen : Shaker, 2009. http://d-nb.info/1159835454/34.

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Wörmann, Julian [Verfasser], Martin [Akademischer Betreuer] Kleinsteuber, Martin [Gutachter] Kleinsteuber, and Walter [Gutachter] Stechele. "Structured Co-sparse Analysis Operator Learning for Inverse Problems in Imaging / Julian Wörmann ; Gutachter: Martin Kleinsteuber, Walter Stechele ; Betreuer: Martin Kleinsteuber." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1205069437/34.

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Tamascelli, Nicola. "A Machine Learning Approach to Predict Chattering Alarms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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The alarm system plays a vital role to grant safety and reliability in the process industry. Ideally, an alarm should inform the operator about critical conditions only; during alarm floods, the operator may be overwhelmed by several alarms in a short time span. Crucial alarms are more likely to be missed during these situations. Poor alarm management is one of the main causes of unintended plant shut down, incidents and near misses in the chemical industry. Most of the alarms triggered during a flood episode are nuisance alarms –i.e. alarms that do not communicate new information to the operator, or alarms that do not require an operator action. Chattering alarms –i.e. that repeat three or more times in a minute, and redundant alarms –i.e. duplicated alarms, are common forms of nuisance. Identifying nuisance alarms is a key step to improve the performance of the alarm system. Advanced techniques for alarm rationalization have been developed, proposing methods to quantify chattering, redundancy and correlation between alarms. Although very effective, these techniques produce static results. Machine Learning appears to be an interesting opportunity to retrieve further knowledge and support these techniques. This knowledge can be used to produce more flexible and dynamic models, as well as to predict alarm behaviour during floods. The aim of this study is to develop a machine learning-based algorithm for real-time alarm classification and rationalization, whose results can be used to support the operator decision-making procedure. Specifically, efforts have been directed towards chattering prediction during alarm floods. Advanced techniques for chattering, redundancy and correlation assessment have been performed on a real industrial alarm database. A modified approach has been developed to dynamically assess chattering, and the results have been used to train three different machine learning models, whose performance has been evaluated and discussed.
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Lee, Ji Hyun. "Development of a Tool to Assist the Nuclear Power Plant Operator in Declaring a State of Emergency Based on the Use of Dynamic Event Trees and Deep Learning Tools." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543069550674204.

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Books on the topic "Operator Learning"

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Publishing, Arco, and Thomson Learning (Firm), eds. Master the emergency dispatcher/911 operator exam. 2nd ed. [Lawrenceville, N.J.?]: Thomson/Peterson's, 2001.

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Learning to solve problems by searching for macro-operators. Boston: Pitman, 1985.

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Peter, Lorange, ed. Implementing strategic processes: Change, learning, and co-operation. Oxford: Blackwell Business, 1993.

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Partnership of Philippine Support Service Agencies, ed. Learning forum on DRR and CSO relief operation. Quezon City]: [Partnership of Philippine Support Service Agencies], 2009.

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1934-, Clarke Walter S., and Herbst Jeffrey Ira, eds. Learning from Somalia: The lessons of armed humanitarian intervention. Boulder, Colo: Westview Press, 1997.

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Elliot-Cannon, Chris. Building a partnership: Co-operation to promote shared learning in the field of learning disability. London: ENB and CCETSW, 1995.

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Burkov, Aleksey. Technical operation of electric ships. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1048423.

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The book investigates the issues related to improving the efficiency of technical operation of ship electric drives, developed their classification. Identified ship's drives, having low reliability, designed and implemented technical solutions to increase their reliability. The appropriateness of the integrated assessment within the tasks of a mathematical and physical modeling. Developed and implemented mathematical and physical models for studies of electric drives. The proposed method, an algorithmic software, and made payments of contactors for work in the proposed technical solutions. Designed for those who specializiruetsya in the field of the theory and practice of ship electric drives. Useful for the learning process in the system of higher Maritime education.
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McGill, Paul. Ireland's learning poor: Adult educational disadvantage and cross-border co-operation. Armagh: Centre for Cross Border Studies, 2001.

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Organisation for Economic Co-operation and Development and SourceOECD (Online service), eds. Learning our lesson: Review of quality teaching in higher education. Paris: OECD, 2010.

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Kontinen, Tiina. Learning challenges of NGOs in development: Co-operation of Finnish NGOs in Morogoro, Tanzania. Helsinki: University of Helsinki, 2007.

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Book chapters on the topic "Operator Learning"

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Durgut, Rafet, and Mehmet Emin Aydin. "Reinforcement Learning-Based Adaptive Operator Selection." In Communications in Computer and Information Science, 29–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85672-4_3.

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Azzopardi, George, and Nicolai Petkov. "Contour Detection by CORF Operator." In Artificial Neural Networks and Machine Learning – ICANN 2012, 395–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33269-2_50.

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Lan, Guanghui. "Operator Sliding and Decentralized Optimization." In First-order and Stochastic Optimization Methods for Machine Learning, 483–566. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1_8.

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Zhang, Mingmin, Bing Wang, Shengle Zhou, and Zhigeng Pan. "Dynamic Gesture Recognition Based on Edge Feature Enhancement Using Sobel Operator." In E-Learning and Games, 152–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65849-0_16.

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Fredouille, Daniel C., Christopher H. Bryant, Channa K. Jayawickreme, Steven Jupe, and Simon Topp. "An ILP Refinement Operator for Biological Grammar Learning." In Inductive Logic Programming, 214–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73847-3_24.

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Towill, D. R. "Selecting Learning Curve Models for Human Operator Performance." In Applications of Human Performance Models to System Design, 403–17. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4757-9244-7_29.

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Seel, Norbert M. "Production Systems and Operator Schemas for Representing Procedural Learning." In Encyclopedia of the Sciences of Learning, 2700–2703. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_915.

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Almohammed, Raghad, and Luay A. AL-Swidi. "Generate a New Types of Fuzzy $$ \Psi _{i} $$-Operator." In Learning and Analytics in Intelligent Systems, 28–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38501-9_3.

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Rokhlin, Dmitry B. "Robbins–Monro Conditions for Persistent Exploration Learning Strategies." In Modern Methods in Operator Theory and Harmonic Analysis, 237–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26748-3_14.

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Chen, Jungan, ShaoZhong Zhang, and Yutian Liu. "A Novel Self Suppression Operator Used in TMA." In Intelligent Data Engineering and Automated Learning – IDEAL 2014, 303–8. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10840-7_37.

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Conference papers on the topic "Operator Learning"

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Montagner, Igor S., Nina S. T. Hirata, and Roberto Hirata. "Image Operator Learning and Applications." In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T). IEEE, 2016. http://dx.doi.org/10.1109/sibgrapi-t.2016.013.

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Volkov, Oleksandr, Mykola Komar, Kateryna Synytsya, and Dmytro Volosheniuk. "THE UAV SIMULATION COMPLEX FOR OPERATOR TRAINING." In International Conference on e-Learning 2019. IADIS Press, 2019. http://dx.doi.org/10.33965/el2019_201909r044.

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Montagner, Igor S., Roberto Hirata, Nina S. T. Hirata, and Stephane Canu. "Kernel Approximations for W-Operator Learning." In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2016. http://dx.doi.org/10.1109/sibgrapi.2016.060.

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Gui-Wu Wei. "Dynamic uncertain linguistic weighted averaging operator." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620836.

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Li, Li-xia, Fa-chao Li, and Shu-xin Luo. "Fuzzy Information Filter Operator and its Application." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258980.

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Kim, Dooroo, and William Singhose. "Human Operator Learning on Double-Pendulum Bridge Cranes." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42994.

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Oscillation of crane payloads makes it challenging to manipulate payloads quickly, accurately, and safely. The problem is compounded when the payload creates a double-pendulum effect. This paper evaluates an input-shaping control method for reducing double-pendulum oscillations. Human operator performance testing on a 10-ton industrial bridge crane is used to verify the effectiveness and robustness of the method. The tests required the operators to drive the crane numerous times over a period of eight days. Data from these experiments show that human operators perform manipulation tasks much faster and safer with the proposed control scheme. Furthermore, considerably less operator effort is required when input shaping is used to limit the oscillation. These experiments also show that significant learning occurred when operators did not have the aid of input shaping. However, the performance never approached that achieved with input shaping without any training. With input shaping enabled, only moderate learning occurred because operators were able to drive the crane near its theoretical limit during their first tests.
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Pramanik, Aniket, and Mathews Jacob. "Improved Model Based Deep Learning Using Monotone Operator Learning (Mol)." In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761520.

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Pramanik, Aniket, and Mathews Jacob. "Improved Model Based Deep Learning Using Monotone Operator Learning (Mol)." In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761520.

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Kurdel, Pavol. "EVALUATION OPERATOR PILOT SKILL IN LEARNING PROCESS." In 15th International Multidisciplinary Scientific GeoConference SGEM2015. Stef92 Technology, 2011. http://dx.doi.org/10.5593/sgem2015/b21/s7.017.

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Wei-Jun Lu, Yan Bai, Yi Tang, and Yan-Fang Tao. "An operator method for semi-supervised learning." In 2009 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2009. http://dx.doi.org/10.1109/icwapr.2009.5207473.

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Reports on the topic "Operator Learning"

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Fan, Yiming. Nonlocal Operator Learning with Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1813660.

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Moore, Nicholas, Eric Cyr, and Christopher Siefert. Learning an Algebriac Multrigrid Interpolation Operator Using a Modified GraphNet Architecture. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1859673.

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Filmer, Deon, Vatsal Nahata, and Shwetlena Sabarwal. Preparation, Practice, and Beliefs: A Machine Learning Approach to Understanding Teacher Effectiveness. Research on Improving Systems of Education (RISE), December 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/084.

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This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data for Math and Kiswahili from Grades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results and outperform standard ordinary least squares in out-of-sample prediction by 14-24 percent. As in previous research, commonly used teacher covariates like teacher gender, education, experience, and so forth are not good predictors of teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations and student surveys) and teacher beliefs (measured through teacher surveys) emerge as much more important. Overall, teacher covariates are stronger predictors of teacher effectiveness in Math than in Kiswahili. Teacher beliefs that they can help disadvantaged and struggling students learn (for Math) and they have good relationships within schools (for Kiswahili), teacher practice of providing written feedback and reviewing key concepts at the end of class (for Math), and spending extra time with struggling students (for Kiswahili) are highly predictive of teacher effectiveness, as is teacher preparation on how to teach foundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematic attention to teacher preparation, practice, and beliefs in teacher research and policy.
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Caponnetto, Andrea, and Yuan Yao. Adaptation for Regularization Operators in Learning Theory. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada456686.

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Caponnetto, Andrea. Optimal Rates for Regularization Operators in Learning Theory. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada456685.

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Aihara, Shimpei, Ryusei Shibata, Ryosuke Mizukami, Takara Sakai, and Akira Shionoya. Electromyograph Estimation of Wheelchair Operators Using Deep Learning. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317554.

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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.
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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.
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Musser, Micah, and Ashton Garriott. Machine Learning and Cybersecurity: Hype and Reality. Center for Security and Emerging Technology, June 2021. http://dx.doi.org/10.51593/2020ca004.

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Cybersecurity operators have increasingly relied on machine learning to address a rising number of threats. But will machine learning give them a decisive advantage or just help them keep pace with attackers? This report explores the history of machine learning in cybersecurity and the potential it has for transforming cyber defense in the near future.
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Clay, Troy A. Stability Operations: Learning from Operation Iraqi Freedom. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada467201.

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