Journal articles: '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’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> </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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Jia, Junying, Haibo Yang, Xin Lu, Mengkun Li, and Yanbo Li. "Operator Behavior Analysis System for Operation Room Based on Deep Learning." Mathematical Problems in Engineering 2022 (March 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/6374040.

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Human behavior analysis has been a leading technology in computer vision in recent years. The station operation room is responsible for the dispatch of trains when they enter and leave the station. By analyzing the behaviors of the operators in the operation room, we can judge whether the operators have violations. However, there is no scheme to analyze the operator’s behavior in the operation room, so we propose an operator behavior analysis system in the station operation room to detect operator’s violations. This paper proposes an improved target tracking algorithm based on Deep-sort. The proposed algorithm can improve the target tracking performance through the actual test compared with the traditional Deep-sort algorithm. In addition, we put forward the detection scheme for common violations in the operation room: off-position, sleeping, and playing mobile phone. Finally, we verify that the proposed algorithm can detect the behaviors of operators in the station operation room in real time.

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Gan, Yaozhong, Zhe Zhang, and Xiaoyang Tan. "Stabilizing Q Learning Via Soft Mellowmax Operator." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7501–9. http://dx.doi.org/10.1609/aaai.v35i9.16919.

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Learning complicated value functions in high dimensional state space by function approximation is a challenging task, partially due to that the max-operator used in temporal difference updates can theoretically cause instability for most linear or non-linear approximation schemes. Mellowmax is a recently proposed differentiable and non-expansion softmax operator that allows a convergent behavior in learning and planning. Unfortunately, the performance bound for the fixed point it converges to remains unclear, and in practice, its parameter is sensitive to various domains and has to be tuned case by case. Finally, the Mellowmax operator may suffer from oversmoothing as it ignores the probability being taken for each action when aggregating them. In this paper we address all the above issues with an enhanced Mellowmax operator, named SM2 (Soft Mellowmax). Particularly, the proposed operator is reliable, easy to implement, and has provable performance guarantee, while preserving all the advantages of Mellowmax. Furthermore, we show that our SM2 operator can be applied to the challenging multi-agent reinforcement learning scenarios, leading to stable value function approximation and state of the art performance.

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Walker, David. "Using machine learning to enhance operator performance." APPEA Journal 60, no. 2 (2020): 681. http://dx.doi.org/10.1071/aj19163.

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Machine learning is a powerful tool to analyse very large datasets. Although machine learning has been used for many years in other areas, such a social media, its value to process industry has only recently been realised. Operators interact with control systems in the same way people interact with social media and, as such, many of the algorithms that have been developed for modelling human interaction are applicable to industrial process operations. Recently, control systems companies have been developing analytical tools to leverage the vast amount of data collected in control systems over many years. These tools enable operations to understand the efficiency of their processes and procedures, identify gaps in their standard operating procedures and measure operator capability. This analysis assists in the improvement of procedures, highlights areas where further training is required and identifies opportunities for procedure automation. This has led to considerable improvements in operation performance, resulting in improved production and reduced downtime. This paper describes what machine learning is, how it can be applied to operation performance, the benefits this provides and possible applications in other areas of the industry.

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Stone, Christopher, and Larry Bull. "For Real! XCS with Continuous-Valued Inputs." Evolutionary Computation 11, no. 3 (September 2003): 299–336. http://dx.doi.org/10.1162/106365603322365315.

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Many real-world problems are not conveniently expressed using the ternary representation typically used by Learning Classifier Systems and for such problems an interval-based representation is preferable. We analyse two interval-based representations recently proposed for XCS, together with their associated operators and find evidence of considerable representational and operator bias. We propose a new interval-based representation that is more straightforward than the previous ones and analyse its bias. The representations presented and their analysis are also applicable to other Learning Classifier System architectures. We discuss limitations of the real multiplexer problem, a benchmark problem used for Learning Classifier Systems that have a continuous-valued representation, and propose a new test problem, the checkerboard problem, that matches many classes of real-world problem more closely than the real multiplexer. Representations and operators are compared using both the real multiplexer and checkerboard problems and we find that representational, operator and sampling bias all affect the performance of XCS in continuous-valued environments.

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Kurniawan, Dwi, Andi Cakravastia Raja, Suprayogi Suprayogi, and Abdul Hakim Halim. "A flow shop batch scheduling and operator assignment model with time-changing effects of learning and forgetting to minimize total actual flow time." Journal of Industrial Engineering and Management 13, no. 3 (November 9, 2020): 546. http://dx.doi.org/10.3926/jiem.3153.

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Purpose: This paper aims to investigate simultaneous problems of batch scheduling and operator assignment with time-changing effects caused by learning and forgetting.Design/methodology/approach: A number of parts will be processed in batches, each of which will be processed through a number of operations where there are alternative operators for each operation bringing different set up and processing times as operators experience different degree of learning and forgetting. A mathematical model is developed for the problems, and the decision variables are operator assignment, the number of batches, batch sizes and the schedule of the resulting batches. A proposed algorithm works by trying different number of batches, starting from one, and increasing the number of batches one by one until the objective function value does not improve anymore.Findings: We show both mathematically and numerically that the closest batch to the due date always becomes the largest batch in the schedule, and the faster operators learn, the larger the difference between the closest batch to the due date and the other batches, the lower optimal number of batches, and the lower the total actual flow time.Originality/value: Previous papers have considered the existence of alternative operators but have not considered learning and forgetting, or have considered learning and forgetting but only in a single-stage system and without considering alternative operators.

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Durgut, Rafet, Mehmet Emin Aydin, and Abdur Rakib. "Transfer Learning for Operator Selection: A Reinforcement Learning Approach." Algorithms 15, no. 1 (January 17, 2022): 24. http://dx.doi.org/10.3390/a15010024.

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In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. However, existing research fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.

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Hara, Keita, Masaki Inoue, and Noboru Sebe. "Learning Koopman Operator under Dissipativity Constraints." IFAC-PapersOnLine 53, no. 2 (2020): 1169–74. http://dx.doi.org/10.1016/j.ifacol.2020.12.1327.

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Durgut, Rafet, Mehmet Emin Aydin, and Ibrahim Atli. "Adaptive operator selection with reinforcement learning." Information Sciences 581 (December 2021): 773–90. http://dx.doi.org/10.1016/j.ins.2021.10.025.

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Montagner, Igor S., Nina S. T. Hirata, and Roberto Hirata. "Staff removal using image operator learning." Pattern Recognition 63 (March 2017): 310–20. http://dx.doi.org/10.1016/j.patcog.2016.10.002.

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García, José, Paola Moraga, Matias Valenzuela, and Hernan Pinto. "A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem." Mathematics 8, no. 4 (April 2, 2020): 507. http://dx.doi.org/10.3390/math8040507.

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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Tong, Wangyu. "A Hybrid Algorithm Framework with Learning and Complementary Fusion Features for Whale Optimization Algorithm." Scientific Programming 2020 (February 18, 2020): 1–25. http://dx.doi.org/10.1155/2020/5684939.

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It has been observed that the structure of whale optimization algorithm (WOA) is good at exploiting capability, but it easily suffers from premature convergence. Hybrid metaheuristics are of the most interesting recent trends for improving the performance of WOA. In this paper, a hybrid algorithm framework with learning and complementary fusion features for WOA is designed, called hWOAlf. First, WOA is integrated with complementary feature operators to enhance exploration capability. Second, the proposed algorithm framework adopts a learning parameter lp according to adaptive adjustment operator to replace the random parameter p. To further verify the efficiency of the hWOAlf, the DE/rand/1 operator of differential evolution (DE) and the mutate operator of backtracking search optimization algorithm (BSA) are embedded into WOA, respectively, to form two new algorithms called WOA-DE and WOA-BSA under the proposed framework. Twenty-three benchmark functions and six engineering design problems are employed to test the performance of WOA-DE and WOA-BSA. Experimental results show that WOA-DE and WOA-BSA are competitive compared with some state-of-the-art algorithms.

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Bacardit, Jaume, and Natalio Krasnogor. "Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems." Evolutionary Computation 17, no. 3 (September 2009): 307–42. http://dx.doi.org/10.1162/evco.2009.17.3.307.

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In this paper we empirically evaluate several local search (LS) mechanisms that heuristically edit classification rules and rule sets to improve their performance. Two kinds of operators are studied, (1) rule-wise operators, which edit individual rules, and (2) a rule set-wise operator, which takes the rules from N parents (N ≥ 2) to generate a new offspring, selecting the minimum subset of candidate rules that obtains maximum training accuracy. Moreover, various ways of integrating these operators within the evolutionary cycle of learning classifier systems are studied. The combinations of LS operators and policies are integrated in a Pittsburgh approach framework that we call MPLCS for memetic Pittsburgh learning classifier system. MPLCS is systematically evaluated using various metrics. Several datasets were employed with the objective of identifying which combination of operators and policies scale well, are robust to noise, generate compact solutions, and use the least amount of computational resources to solve the problems.

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ELMOATAZ, A., X. DESQUESNES, and M. TOUTAIN. "On the game p-Laplacian on weighted graphs with applications in image processing and data clustering." European Journal of Applied Mathematics 28, no. 6 (July 3, 2017): 922–48. http://dx.doi.org/10.1017/s0956792517000122.

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Game-theoretic p-Laplacian or normalized p-Laplacian operator is a version of classical variational p-Laplacian which was introduced recently in connection with stochastic games called Tug-of-War with noise (Peres et al. 2008, Tug-of-war with noise: A game-theoretic view of the p-laplacian. Duke Mathematical Journal145(1), 91–120). In this paper, we propose an adaptation and generalization of this operator on weighted graphs for 1 ≤ p ≤ ∞. This adaptation leads to a partial difference operator which is a combination between 1-Laplace, infinity-Laplace and 2-Laplace operators on graphs. Then we consider the Dirichlet problem associated to this operator and we prove the uniqueness and existence of the solution. We show that the solution leads to an iterative non-local average operator on graphs. Finally, we propose to use this operator as a unified framework for interpolation problems in signal processing on graphs, such as image processing and machine learning.

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Kveton, Branislav, and Georgios Theocharous. "Structured Kernel-Based Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 569–75. http://dx.doi.org/10.1609/aaai.v27i1.8669.

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Kernel-based reinforcement learning (KBRL) is a popular approach to learning non-parametric value function approximations. In this paper, we present structured KBRL, a paradigm for kernel-based RL that allows for modeling independencies in the transition and reward models of problems. Real-world problems often exhibit this structure and can be solved more efficiently when it is modeled. We make three contributions. First, we motivate our work, define a structured backup operator, and prove that it is a contraction. Second, we show how to evaluate our operator efficiently. Our analysis reveals that the fixed point of the operator is the optimal value function in a special factored MDP. Finally, we evaluate our method on a synthetic problem and compare it to two KBRL baselines. In most experiments, we learn better policies than the baselines from an order of magnitude less training data.

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Lei, Yinbin, and Jun Zhang. "Closure System and Its Semantics." Axioms 10, no. 3 (August 23, 2021): 198. http://dx.doi.org/10.3390/axioms10030198.

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It is well known that topological spaces are axiomatically characterized by the topological closure operator satisfying the Kuratowski Closure Axioms. Equivalently, they can be axiomatized by other set operators encoding primitive semantics of topology, such as interior operator, exterior operator, boundary operator, or derived-set operator (or dually, co-derived-set operator). It is also known that a topological closure operator (and dually, a topological interior operator) can be weakened into generalized closure (interior) systems. What about boundary operator, exterior operator, and derived-set (and co-derived-set) operator in the weakened systems? Our paper completely answers this question by showing that the above six set operators can all be weakened (from their topological counterparts) in an appropriate way such that their inter-relationships remain essentially the same as in topological systems. Moreover, we show that the semantics of an interior point, an exterior point, a boundary point, an accumulation point, a co-accumulation point, an isolated point, a repelling point, etc. with respect to a given set, can be extended to an arbitrary subset system simply by treating the subset system as a base of a generalized interior system (and hence its dual, a generalized closure system). This allows us to extend topological semantics, namely the characterization of points with respect to an arbitrary set, in terms of both its spatial relations (interior, exterior, or boundary) and its dynamic convergence of any sequence (accumulation, co-accumulation, and isolation), to much weakened systems and hence with wider applicability. Examples from the theory of matroid and of Knowledge/Learning Spaces are used as an illustration.

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Su, Yong, Jiawei Jin, Weilong Peng, Keke Tang, Asad Khan, Simin An, and Meng Xing. "A Convex Relaxation Approach for Learning the Robust Koopman Operator." Wireless Communications and Mobile Computing 2022 (June 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/5010251.

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Although data-driven models, especially deep learning, have achieved astonishing results on many prediction tasks for nonlinear sequences, challenges still remain in finding an appropriate way to embed prior knowledge of physical dynamics in these models. In this work, we introduce a convex relaxation approach for learning robust Koopman operators of nonlinear dynamical systems, which are intended to construct approximate space spanned by eigenfunctions of the Koopman operator. Different from the classical dynamic mode decomposition, we use the layer weights of neural networks as eigenfunctions of the Koopman operator, providing intrinsic coordinates that globally linearize the dynamics. We find that the approximation of space can be regarded as an orthogonal Procrustes problem on the Stiefel manifold, which is highly sensitive to noise. The key contribution of this paper is to demonstrate that strict orthogonal constraint can be replaced by its convex relaxation, and the performance of the model can be improved without increasing the complexity when dealing with both clean and noisy data. After that, the overall model can be optimized via backpropagation in an end-to-end manner. The comparisons of the proposed method against several state-of-the-art competitors are shown on nonlinear oscillators and the lid-driven cavity flow.

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Zhou, Bo, Yi Chao Fan, YuXin Liu, and XuDong Yin. "Multi-operator feature enhancement methods for industrial defect detection." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012030. http://dx.doi.org/10.1088/1742-6596/2078/1/012030.

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Abstract Deep learning based object detection algorithms have been gradually applied to industrial defect detection, but the resulted accuracy does not fully meet the needs of industrial inspection. In order to enhance image features, this paper proposes a series of image preprocessing schemes based on edge detection operators, using a single-operator preprocessing scheme, a multi-operator serial preprocessing scheme and a multi-operator parallel preprocessing scheme for image preprocessing of data to enhance the edge features of images. The validation experiment of the SSD based object detection algorithm is performed on dataset used for industrial inspection, to verify the effectiveness of the processing schemes above. The result shows that the multi-operator based image preprocessing method is effective in improving the accuracy of surface defect detection in the field of industrial defect detection.

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Miller, Sarah M., and Wai-Tat Fu. "The Role of Temporal Sequence Learning in Guiding Visual Attention Allocation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 19 (October 2007): 1368–72. http://dx.doi.org/10.1177/154193120705101920.

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Models of visual attention allocation suggest that monitoring is driven primarily by proximal cues like bandwidth and value. However, these cues might not always be predictive of the meaningful events an operator is asked to monitor. The aim of the current study is to extend visual sampling models by studying whether sampling can be influenced by more distal cues, like detecting patterns in the monitored signal, when proximal cues, like bandwidth, are not predictive of the meaningful events the operator is asked to monitor. Ten participants completed a task based on Senders' (1964) experiment where operators were asked to monitor a series of four gauges to detect when the gauges traveled into the alarm region. The performance results suggest that participants could successfully adapt to the temporal sequence. However, participants did not show explicit awareness of the sequence, indicating that this type of learning could, in some cases, be implicit. Implications for display design and training are discussed.

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Kim, Dae Won, Song Ko, and Bo Yeong Kang. "Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning." Applied Mechanics and Materials 284-287 (January 2013): 3093–96. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3093.

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Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms, providing effective and efficient optimization performance in a variety of research areas. Recent studies have proposed new EDAs that employ mutation operators in standard EDAs to increase the population diversity. We present a new mutation operator, a matrix transpose, specifically designed for Bayesian structure learning, and we evaluate its performance in Bayesian structure learning. The results indicate that EDAs with transpose mutation give markedly better performance than conventional EDAs.

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Xiao, Wei Yue, Yue Hua Cai, and You Xin Luo. "Evolutionary Cellular Automata Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization." Applied Mechanics and Materials 271-272 (December 2012): 912–16. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.912.

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The optimization design about hybrid discrete variables synthesizing integer, discrete and continuous variables is very significant but also difficult in engineering, mathematics for programming and operational research. Aimed at shortages of existing optimum methods, in this paper, Evolutionary Cellar Automata Algorithm (ECAA) is proposed to complex optimization problem with hybrid discrete variables which has a digging operator and two learning operators (dual arithmetic crossover operator and chaos-peak-jumping operator). The computing examples of mechanical optimization design show that this algorithm has no special requirements on the characteristics of optimal designing problems, it has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.

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Yuan, Gui Li, Yan Guang Xue, and Qing Jiao Liang. "The Design of Adaptive Immune Vaccine Algorithm." Advanced Materials Research 308-310 (August 2011): 1094–98. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1094.

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Aiming at disadvantages of Genetic Algorithm (GA) and learning from the immune system theory, this paper introduces immune memory cell of immune theory, vaccine extraction and vaccination operator based on immune theory, and adaptive probability crossover and mutation operator to GA, to improve the optimization ability and search efficiency of GA, and proposes Adaptive Immune Vaccine Algorithm (AIVA). Then proves the convergence of the algorithm, gives the composition mechanisms of the key operators, and verifies the role of each operator. Finally, four test functions have been optimized using GA, AIGA and AIVA. The experimental results show that AIVA effectively overcomes the GA Defects, greatly prevents the degradation of population, and has perfect convergence stability and excellent global optimization capability.

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Tan, Jess, Desmond Yeo, Rachael Neoh, Huey-Eng Chua, and Sourav S. Bhowmick. "MOCHA." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3602–5. http://dx.doi.org/10.14778/3554821.3554854.

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The database systems course is offered in many major universities. A key learning goal of learners taking such a course is to understand how sql queries are processed in an RDBMS in practice. To this end, comprehension of the impact of various physical operators on the selected query execution plan (QEP) of a query is paramount. Unfortunately, off-the-shelf RDBMS typically only expose the QEP to users without revealing information about the impact of alternative choices of various physical operators on it in a user-friendly manner to aid learning. In this demonstration, we present a novel system called MOCHA that facilitates exploration and visualization of the impact of alternative physical operator choices on the QEP of a given SQL query. MOCHA accepts an SQL query as input, and compares and visualizes the QEP and alternative plans which are selected based on learner-specified operator preferences. Furthermore, it intuitively explains why the key operators in a QEP are chosen by connecting them to established knowledge in the literature.

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Gnecco, Giorgio, Marco Gori, and Marcello Sanguineti. "Learning with Boundary Conditions." Neural Computation 25, no. 4 (April 2013): 1029–106. http://dx.doi.org/10.1162/neco_a_00417.

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Kernel machines traditionally arise from an elegant formulation based on measuring the smoothness of the admissible solutions by the norm in the reproducing kernel Hilbert space (RKHS) generated by the chosen kernel. It was pointed out that they can be formulated in a related functional framework, in which the Green’s function of suitable differential operators is thought of as a kernel. In this letter, our own picture of this intriguing connection is given by emphasizing some relevant distinctions between these different ways of measuring the smoothness of admissible solutions. In particular, we show that for some kernels, there is no associated differential operator. The crucial relevance of boundary conditions is especially emphasized, which is in fact the truly distinguishing feature of the approach based on differential operators. We provide a general solution to the problem of learning from data and boundary conditions and illustrate the significant role played by boundary conditions with examples. It turns out that the degree of freedom that arises in the traditional formulation of kernel machines is indeed a limitation, which is partly overcome when incorporating the boundary conditions. This likely holds true in many real-world applications in which there is prior knowledge about the expected behavior of classifiers and regressors on the boundary.

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ASKER, LARS, MATS DANIELSON, and LOVE EKENBERG. "COMMITTEES OF LEARNING AGENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 02 (April 2000): 187–202. http://dx.doi.org/10.1142/s0218488500000137.

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We describe how machine learning and decision theory is combined in an application that supports control room operators of a combined heating and power plant to cope with the overwhelming complexity of situations when severe plant disturbances occur. The application is designed as an assistant, rather than as an automatic system that intervenes directly in the operator/plant loop. The application is required to handle vague and numerically imprecise background information in the construction of classifier committees. A classifier committee (or ensemble) is a classifier created by combining the predictions of multiple sub-classifiers. The presented method combines classifiers into a committee by using computational methods for decision analysis that are designed to work when the information at hand is imprecise. The application evaluates and make priorities between classified alarms according to credibilities that depend on the current context. Machine learning techniques are used to construct classifiers that recognize various malfunctions in a process, determine whether a situation is normal or not, and make priorities among alarms.

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ARIF, M., and H. INOOKA. "HUMAN LEARNING CHARACTERISTICS IN THE TRACKING TASKS OF ITERATIVE NATURE." International Journal of Neural Systems 09, no. 04 (August 1999): 301–10. http://dx.doi.org/10.1142/s0129065799000307.

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In this paper, human learning characteristics in the tracking tasks of iterative nature are investigated. Various linear and nonlinear systems are used as plant, and a human operator has to generate the proper control inputs to force these systems in tracking the desired trajectory. The learning behaviour of the human operator in modifying his control actions is studied and it is observed that the human operator can improve his performance quite efficiently despite the unavailability of any information about the system or the desired trajectories. It is concluded from the experiments that the human operator not only use the information that is directly available to him (error in this case), but also extracts some useful information (e.g. error rate) that he feels is necessary to generate a good control action. The limitation of the human performance is studied in frequency domain, and the performance of the human operator against the frequency bandwidth of error and error rate signals are highlighted. Analysis of the results revealed that a human operator gives more importance to the error rate in generating his control actions and, accordingly, it is observed that his limitation in term of performance is more sensitive to the frequency bandwidth of the error rate as compared to the error. The human operator cannot improve his performance once the frequency components of the error or error rates shift to the higher frequencies, say above 1.0 Hz.

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Liu, Ruilin, Sebastián V. Romero, Izaskun Oregi, Eneko Osaba, Esther Villar-Rodriguez, and Yue Ban. "Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States." Entropy 24, no. 11 (October 25, 2022): 1529. http://dx.doi.org/10.3390/e24111529.

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Coherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning, and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder operators, i.e., creation and annihilation operators. The high fidelity of the digitally generated coherent states is verified compared with the Poissonian distribution in Fock space. Secondly, by using Variational Quantum Algorithms, we choose different ansatzes to generate coherent states. The quantum resources—such as numbers of quantum gates, layers and iterations—are analyzed for quantum circuit learning. The simulation results show that quantum circuit learning can provide high fidelity on learning coherent states by choosing appropriate ansatzes.

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Chun, Il Yong, and Jeffrey A. Fessler. "Convolutional Analysis Operator Learning: Acceleration and Convergence." IEEE Transactions on Image Processing 29 (2020): 2108–22. http://dx.doi.org/10.1109/tip.2019.2937734.

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Steinke, Florian, and Bernhard Schölkopf. "MACHINE LEARNING METHODS FOR ESTIMATING OPERATOR EQUATIONS." IFAC Proceedings Volumes 39, no. 1 (2006): 1192–97. http://dx.doi.org/10.3182/20060329-3-au-2901.00192.

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Hamner, Bradley, Sanjiv Singh, and Sebastian Scherer. "Learning obstacle avoidance parameters from operator behavior." Journal of Field Robotics 23, no. 11-12 (November 2006): 1037–58. http://dx.doi.org/10.1002/rob.20171.

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Surasinghe, Sudam, and Erik M. Bollt. "Randomized Projection Learning Method for Dynamic Mode Decomposition." Mathematics 9, no. 21 (November 4, 2021): 2803. http://dx.doi.org/10.3390/math9212803.

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A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduced dimensional space. In practical applications, snapshots are in a high-dimensional observable space and the DMD operator matrix is massive. Hence, computing DMD with the full spectrum is expensive, so our main computational goal is to estimate the eigenvalue and eigenvectors of the DMD operator in a projected domain. We generalize the current algorithm to estimate a projected DMD operator. We focus on a powerful and simple random projection algorithm that will reduce the computational and storage costs. While, clearly, a random projection simplifies the algorithmic complexity of a detailed optimal projection, as we will show, the results can generally be excellent, nonetheless, and the quality could be understood through a well-developed theory of random projections. We will demonstrate that modes could be calculated for a low cost by the projected data with sufficient dimension.

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Zhao, Bobo, and Tao Tang. "Evaluating the Learning Performance of Emergency Procedures for Operators in Metro Based on a Cognition Model." Promet - Traffic&Transportation 32, no. 3 (May 12, 2020): 335–45. http://dx.doi.org/10.7307/ptt.v32i3.3241.

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With the rapid development of the automated metro, valid emergency procedures play a significant role for operators in metro emergency response and recovery. Also, the operators have a challenge to learn different emergency procedures under different automation grades of the metro. Hence, the paper aims to evaluate the learning performance of emergency procedures with regard to the operator. Based on the ACT-R cognitive theory, two decision patterns of the operators are proposed to predict the operator’s learning process for emergency procedures, and a cognition model including 16 production rules and 32 chunks to realize the perceptual encoding and the corresponding determining parts is built. After that, an experiment is further implemented to validate the model results.

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Vojtig, Peter. "Fuzzy Reasoning with Tunable t-Operators." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 4 (August 20, 1998): 121–27. http://dx.doi.org/10.20965/jaciii.1998.p0121.

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We introduce a model of fuzzy logic programming in a truth functional fuzzy logic with arbitrary and/or tunable t-operators. This t-operator tuning is the subject of different learning from neural networks to evolutionary calculation. The choice of an operator mostly depends on the real world problem modeled, often depending on user environments and/or stereotypes. To model aggregations of different witnesses, our rules have body in disjunctive normal form. We develop fuzzy fixpoint theory and show soundness and completeness of our semantics. To control calculational efficiency, we introduce a cut with threshold. For knowledge mining and tuning of the t-operator, we restrict the problem to finding a tnorm fitting finitely many values. We show that our model of fuzzy logic programs semantically coincides with a fuzzy controller model.

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Alford-Lago, D. J., C. W. Curtis, A. T. Ihler, and O. Issan. "Deep learning enhanced dynamic mode decomposition." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 3 (March 2022): 033116. http://dx.doi.org/10.1063/5.0073893.

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Koopman operator theory shows how nonlinear dynamical systems can be represented as an infinite-dimensional, linear operator acting on a Hilbert space of observables of the system. However, determining the relevant modes and eigenvalues of this infinite-dimensional operator can be difficult. The extended dynamic mode decomposition (EDMD) is one such method for generating approximations to Koopman spectra and modes, but the EDMD method faces its own set of challenges due to the need of user defined observables. To address this issue, we explore the use of autoencoder networks to simultaneously find optimal families of observables, which also generate both accurate embeddings of the flow into a space of observables and submersions of the observables back into flow coordinates. This network results in a global transformation of the flow and affords future state prediction via the EDMD and the decoder network. We call this method the deep learning dynamic mode decomposition (DLDMD). The method is tested on canonical nonlinear data sets and is shown to produce results that outperform a standard DMD approach and enable data-driven prediction where the standard DMD fails.

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Halim, Abd, Abdul Muis, Abdul Halik, and Muhammad Saiful. "Rancang Bangun Alat Simulasi Wiper Otomatis Berbasis Microcontroller Sebagai Media Pembelajaran Wiper Electrical System Alat Berat." MEDIA PERSPEKTIF : Journal of Technology 12, no. 1 (June 28, 2020): 42. http://dx.doi.org/10.46964/jtmp.v12i1.431.

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A wiper is a device used to clean rainwater and particles from glass that can block the vision of heavy equipment operators during rainy weather. To clean rainwater from the glass, the operator must activate the wiper switch. When the operator is focused on work often the operator forgets to turn on the windshield wipers or turns off the windshield wipers, it can disturb the operator's concentration especially on the machine when working. Automatic wipers are devices that detect rainwater with the help of a raindrop sensor FC 37 to activate the wiper motor without the need to activate the switch. With this automatic wiper the operator can comfortably work when the weather is rainy. The purpose of making a microcontroller arduino ino R3 based automatic wiper simulation tool is as a learning media for wiper electrical system applications on heavy equipment. The results obtained from simulation testing based on the standard operating procedures that have been made and successfully work well without problems.

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Igarashi, Hiroshi. "Subliminal Calibration for Machine Operation." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 1 (January 20, 2012): 108–16. http://dx.doi.org/10.20965/jaciii.2012.p0108.

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This paper proposes a skill assist technique without having the operator to be aware of it. Heretofore, many operation assists in a human-machine system has added artificial force in human operation input such as reactive force from obstacles. Such an approach is suitable in a particular task as simulated by the designer, because it can improve safety and efficiency, but is simultaneously hindering human learning ability. The proposed method will correct the machine dynamics of the operation subject subliminally, meaning that the operator will not be aware that it is being altered. Henceforth, it will be possible to enhance operability, without having to prevent the human learning ability. As a result of a verification experiment on 20 test subjects, it has been clarified that it is possible to enhance the operation performance without the operators knowing of the assist.

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Willems, Jannes J., and Tim Busscher. "Institutional Barriers and Bridges for Climate Proofing Waterway Infrastructures." Public Works Management & Policy 24, no. 1 (September 28, 2018): 63–70. http://dx.doi.org/10.1177/1087724x18798383.

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Although the urgency for climate proofing waterway assets grows, to date, little is known about the organizational learning process of infrastructure operators to address this urgency. Climate proofing infrastructure increasingly requires infrastructure operators to rethink the original aims of their networks (such as bringing prosperity by enabling transportation), which relates to the notion of double-loop learning. The goal of this article is to identify institutional barriers and bridges that condition learning processes of infrastructure operators in climate proofing waterway infrastructures. This article is based on a case study of the Dutch national inland waterway network. Our findings suggest that climate proofing infrastructure requires an integrative and inclusive approach, in which the focus on waterway assets is loosened and infrastructure operators become more oriented towards wider, larger regional developments. However, the barriers and bridges encountered in the case study suggest that the Dutch waterway operator Rijkswaterstaat mainly focuses on refining and optimizing the current waterway network, i.e., single-loop learning. The questioning of underlying values, i.e., double-loop learning, is more complicated and has to be actively organized.

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Xi, Bao, Shuo Wang, Xuemei Ye, Yinghao Cai, Tao Lu, and Rui Wang. "A robotic shared control teleoperation method based on learning from demonstrations." International Journal of Advanced Robotic Systems 16, no. 4 (July 2019): 172988141985742. http://dx.doi.org/10.1177/1729881419857428.

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In teleoperation, the operator is often required to command the motion of the remote robot and monitor its behavior. However, such an interaction demands a heavy workload from a human operator when facing with complex tasks and dynamic environments. In this article, we propose a shared control method to assist the operator in the manipulation tasks to reduce the workload and improve the efficiency. We adopt a task-parameterized hidden semi-Markov model to learn a manipulation skill from several human demonstrations. We utilize the learned model to predict the manipulation target given the current observed robotic motion trajectory and subsequently estimate the desired robotic motion given the current input of the operator. The estimated robotic motion is then utilized to correct the input of the operator to provide manipulation assistance. In addition, a set of virtual reality devices are used to capture the operator’s motion and display the vision feedback from the remote site. We evaluate our approach through two manipulation tasks with a dual-arm robot. The experimental results show the effectiveness of the proposed method.

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ANTONIOU, I., and F. BOSCO. "ON THE SPECTRAL PROPERTIES OF A MARKOV MODEL FOR LEARNING PROCESSES." International Journal of Modern Physics C 11, no. 02 (March 2000): 213–20. http://dx.doi.org/10.1142/s0129183100000201.

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We study the spectral properties of the evolution operator of densities of the one-dimensional version of a Markov model for instrumental conditioning proposed by Bush and Mosteller in the early '50s. The model is a probabilistic combination of dynamical systems with phase space dependent probability distribution. The spectral properties are derived by restricting the stochastic operator in the space of analytic functions. The nonergodic properties of the model are demonstrated by the spectrum of the stochastic operator.

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Cao, Xiaokai, Michal Fečkan, Dong Shen, and JinRong Wang. "Iterative learning control for impulsive multi-agent systems with varying trial lengths." Nonlinear Analysis: Modelling and Control 27 (January 26, 2022): 1–21. http://dx.doi.org/10.15388/namc.2022.27.25475.

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In this paper, we introduce iterative learning control (ILC) schemes with varying trial lengths (VTL) to control impulsive multi-agent systems (I-MAS). We use domain alignment operator to characterize each tracking error to ensure that the error can completely update the control function during each iteration. Then we analyze the system’s uniform convergence to the target leader. Further, we use two local average operators to optimize the control function such that it can make full use of the iteration error. Finally, numerical examples are provided to verify the theoretical results.

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Liu, Xiaohan, Xiaoguang Gao, Zidong Wang, and Xinxin Ru. "Improved Local Search with Momentum for Bayesian Networks Structure Learning." Entropy 23, no. 6 (June 15, 2021): 750. http://dx.doi.org/10.3390/e23060750.

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Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

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Journal articles on the topic 'Operator Learning'

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