1,659 research outputs found

    A manifold learning approach for integrated computational materials engineering

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    Image-based simulation is becoming an appealing technique to homogenize properties of real microstructures of heterogeneous materials. However fast computation techniques are needed to take decisions in a limited time-scale. Techniques based on standard computational homogenization are seriously compromised by the real-time constraint. The combination of model reduction techniques and high performance computing contribute to alleviate such a constraint but the amount of computation remains excessive in many cases. In this paper we consider an alternative route that makes use of techniques traditionally considered for machine learning purposes in order to extract the manifold in which data and fields can be interpolated accurately and in real-time and with minimum amount of online computation. Locallly Linear Embedding is considered in this work for the real-time thermal homogenization of heterogeneous microstructures

    Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data

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    Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm

    A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity

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    Standard simulation in classical mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy, ...), whereas the second one consists of models that scientists have extracted from collected, natural or synthetic data. Even if one can be confident on the first type of equations, the second one contains modeling errors. Moreover, this second type of equations remains too particular and often fails in describing new experimental results. The vast majority of existing models lack of generality, and therefore must be constantly adapted or enriched to describe new experimental findings. In this work we propose a new method, able to directly link data to computers in order to perform numerical simulations. These simulations will employ axiomatic, universal laws while minimizing the need of explicit, often phenomenological, models. This technique is based on the use of manifold learning methodologies, that allow to extract the relevant information from large experimental datasets

    Face Recognition Methodologies Using Component Analysis: The Contemporary Affirmation of The Recent Literature

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    This paper explored the contemporary affirmation of the recent literature in the context of face recognition systems, a review motivated by contradictory claims in the literature. This paper shows how the relative performance of recent claims based on methodologies such as PCA and ICA, which are depend on the task statement. It then explores the space of each model acclaimed in recent literature. In the process, this paper verifies the results of many of the face recognition models in the literature, and relates them to each other and to this work

    New electronic tongue sensor array system for accurate liquor beverage classification

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    The use of sensors in different applications to improve the monitoring of a process and its variables is required as it enables information to be obtained directly from the process by ensuring its quality. This is now possible because of the advances in the fabrication of sensors and the development of equipment with a high processing capability. These elements enable the development of portable smart systems that can be used directly in the monitoring of the process and the testing of variables, which, in some cases, must evaluated by laboratory tests to ensure high-accuracy measurement results. One of these processes is taste recognition and, in general, the classification of liquids, where electronic tongues have presented some advantages compared with traditional monitoring because of the time reduction for the analysis, the possibility of online monitoring, and the use of strategies of artificial intelligence for the analysis of the data. However, although some methods and strategies have been developed, it is necessary to continue in the development of strategies that enable the results in the analysis of the data from electrochemical sensors to be improved. In this way, this paper explores the application of an electronic tongue system in the classification of liquor beverages, which was directly applied to an alcoholic beverage found in specific regions of Colombia. The system considers the use of eight commercial sensors and a data acquisition system with a machine-learning-based methodology developed for this aim. Results show the advantages of the system and its accuracy in the analysis and classification of this kind of alcoholic beverage.This research was funded by the Department of Science, Technology and Innovation of Colombia, grant 799, and Universidad Nacional de Colombia, grant 57399.Peer ReviewedPostprint (published version

    Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

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    Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling softwareโ€™s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulationโ€™s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modelingโ€™s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

    ๋งค๊ฐœ๋ถ„ํฌ๊ทผ์‚ฌ๋ฅผ ํ†ตํ•œ ๊ณต์ •์‹œ์Šคํ…œ ๊ณตํ•™์—์„œ์˜ ํ™•๋ฅ ๊ธฐ๊ณ„ํ•™์Šต ์ ‘๊ทผ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2021.8. ์ด์ข…๋ฏผ.With the rapid development of measurement technology, higher quality and vast amounts of process data become available. Nevertheless, process data are โ€˜scarceโ€™ in many cases as they are sampled only at certain operating conditions while the dimensionality of the system is large. Furthermore, the process data are inherently stochastic due to the internal characteristics of the system or the measurement noises. For this reason, uncertainty is inevitable in process systems, and estimating it becomes a crucial part of engineering tasks as the prediction errors can lead to misguided decisions and cause severe casualties or economic losses. A popular approach to this is applying probabilistic inference techniques that can model the uncertainty in terms of probability. However, most of the existing probabilistic inference techniques are based on recursive sampling, which makes it difficult to use them for industrial applications that require processing a high-dimensional and massive amount of data. To address such an issue, this thesis proposes probabilistic machine learning approaches based on parametric distribution approximation, which can model the uncertainty of the system and circumvent the computational complexity as well. The proposed approach is applied for three major process engineering tasks: process monitoring, system modeling, and process design. First, a process monitoring framework is proposed that utilizes a probabilistic classifier for fault classification. To enhance the accuracy of the classifier and reduce the computational cost for its training, a feature extraction method called probabilistic manifold learning is developed and applied to the process data ahead of the fault classification. We demonstrate that this manifold approximation process not only reduces the dimensionality of the data but also casts the data into a clustered structure, making the classifier have a low dependency on the type and dimension of the data. By exploiting this property, non-metric information (e.g., fault labels) of the data is effectively incorporated and the diagnosis performance is drastically improved. Second, a probabilistic modeling approach based on Bayesian neural networks is proposed. The parameters of deep neural networks are transformed into Gaussian distributions and trained using variational inference. The redundancy of the parameter is autonomously inferred during the model training, and insignificant parameters are eliminated a posteriori. Through a verification study, we demonstrate that the proposed approach can not only produce high-fidelity models that describe the stochastic behaviors of the system but also produce the optimal model structure. Finally, a novel process design framework is proposed based on reinforcement learning. Unlike the conventional optimization methods that recursively evaluate the objective function to find an optimal value, the proposed method approximates the objective function surface by parametric probabilistic distributions. This allows learning the continuous action policy without introducing any cumbersome discretization process. Moreover, the probabilistic policy gives means for effective control of the exploration and exploitation rates according to the certainty information. We demonstrate that the proposed framework can learn process design heuristics during the solution process and use them to solve similar design problems.๊ณ„์ธก๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ์–‘์งˆ์˜, ๊ทธ๋ฆฌ๊ณ  ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ๊ณต์ • ๋ฐ์ดํ„ฐ์˜ ์ทจ๋“์ด ๊ฐ€๋Šฅํ•ด์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋งŽ์€ ๊ฒฝ์šฐ ์‹œ์Šคํ…œ ์ฐจ์›์˜ ํฌ๊ธฐ์— ๋น„ํ•ด์„œ ์ผ๋ถ€ ์šด์ „์กฐ๊ฑด์˜ ๊ณต์ • ๋ฐ์ดํ„ฐ๋งŒ์ด ์ทจ๋“๋˜๊ธฐ ๋•Œ๋ฌธ์—, ๊ณต์ • ๋ฐ์ดํ„ฐ๋Š” โ€˜ํฌ์†Œโ€™ํ•˜๊ฒŒ ๋œ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๊ณต์ • ๋ฐ์ดํ„ฐ๋Š” ์‹œ์Šคํ…œ ๊ฑฐ๋™ ์ž์ฒด์™€ ๋”๋ถˆ์–ด ๊ณ„์ธก์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋…ธ์ด์ฆˆ๋กœ ์ธํ•œ ๋ณธ์งˆ์ ์ธ ํ™•๋ฅ ์  ๊ฑฐ๋™์„ ๋ณด์ธ๋‹ค. ๋”ฐ๋ผ์„œ ์‹œ์Šคํ…œ์˜ ์˜ˆ์ธก๋ชจ๋ธ์€ ์˜ˆ์ธก ๊ฐ’์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ๊ธฐ์ˆ ํ•˜๋Š” ๊ฒƒ์ด ์š”๊ตฌ๋˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์˜ค์ง„์„ ์˜ˆ๋ฐฉํ•˜๊ณ  ์ž ์žฌ์  ์ธ๋ช… ํ”ผํ•ด์™€ ๊ฒฝ์ œ์  ์†์‹ค์„ ๋ฐฉ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋Œ€ํ•œ ๋ณดํŽธ์ ์ธ ์ ‘๊ทผ๋ฒ•์€ ํ™•๋ฅ ์ถ”์ •๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ด๋Ÿฌํ•œ ๋ถˆํ™•์‹ค์„ฑ์„ ์ •๋Ÿ‰ํ™” ํ•˜๋Š” ๊ฒƒ์ด๋‚˜, ํ˜„์กดํ•˜๋Š” ์ถ”์ •๊ธฐ๋ฒ•๋“ค์€ ์žฌ๊ท€์  ์ƒ˜ํ”Œ๋ง์— ์˜์กดํ•˜๋Š” ํŠน์„ฑ์ƒ ๊ณ ์ฐจ์›์ด๋ฉด์„œ๋„ ๋‹ค๋Ÿ‰์ธ ๊ณต์ •๋ฐ์ดํ„ฐ์— ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋งค๊ฐœ๋ถ„ํฌ๊ทผ์‚ฌ์— ๊ธฐ๋ฐ˜ํ•œ ํ™•๋ฅ ๊ธฐ๊ณ„ํ•™์Šต์„ ์ ์šฉํ•˜์—ฌ ์‹œ์Šคํ…œ์— ๋‚ด์žฌ๋œ ๋ถˆํ™•์‹ค์„ฑ์„ ๋ชจ๋ธ๋งํ•˜๋ฉด์„œ๋„ ๋™์‹œ์— ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ์ ‘๊ทผ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ €, ๊ณต์ •์˜ ๋ชจ๋‹ˆํ„ฐ๋ง์— ์žˆ์–ด ๊ฐ€์šฐ์‹œ์•ˆ ํ˜ผํ•ฉ ๋ชจ๋ธ (Gaussian mixture model)์„ ๋ถ„๋ฅ˜์ž๋กœ ์‚ฌ์šฉํ•˜๋Š” ํ™•๋ฅ ์  ๊ฒฐํ•จ ๋ถ„๋ฅ˜ ํ”„๋ ˆ์ž„์›Œํฌ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ด๋•Œ ๋ถ„๋ฅ˜์ž์˜ ํ•™์Šต์—์„œ์˜ ๊ณ„์‚ฐ ๋ณต์žก๋„๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ €์ฐจ์›์œผ๋กœ ํˆฌ์˜์‹œํ‚ค๋Š”๋ฐ, ์ด๋ฅผ ์œ„ํ•œ ํ™•๋ฅ ์  ๋‹ค์–‘์ฒด ํ•™์Šต (probabilistic manifold learn-ing) ๋ฐฉ๋ฒ•์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ๋ฐ์ดํ„ฐ์˜ ๋‹ค์–‘์ฒด (manifold)๋ฅผ ๊ทผ์‚ฌํ•˜์—ฌ ๋ฐ์ดํ„ฐ ํฌ์ธํŠธ ์‚ฌ์ด์˜ ์Œ๋ณ„ ์šฐ๋„ (pairwise likelihood)๋ฅผ ๋ณด์กดํ•˜๋Š” ํˆฌ์˜๋ฒ•์ด ์‚ฌ์šฉ๋œ๋‹ค. ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๋ฐ์ดํ„ฐ์˜ ์ข…๋ฅ˜์™€ ์ฐจ์›์— ์˜์กด๋„๊ฐ€ ๋‚ฎ์€ ์ง„๋‹จ ๊ฒฐ๊ณผ๋ฅผ ์–ป์Œ๊ณผ ๋™์‹œ์— ๋ฐ์ดํ„ฐ ๋ ˆ์ด๋ธ”๊ณผ ๊ฐ™์€ ๋น„๊ฑฐ๋ฆฌ์  (non-metric) ์ •๋ณด๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์‚ฌ์šฉํ•˜์—ฌ ๊ฒฐํ•จ ์ง„๋‹จ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋‘˜์งธ๋กœ, ๋ฒ ์ด์ง€์•ˆ ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง(Bayesian deep neural networks)์„ ์‚ฌ์šฉํ•œ ๊ณต์ •์˜ ํ™•๋ฅ ์  ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ•๋ก ์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ์‹ ๊ฒฝ๋ง์˜ ๊ฐ ๋งค๊ฐœ๋ณ€์ˆ˜๋Š” ๊ฐ€์šฐ์Šค ๋ถ„ํฌ๋กœ ์น˜ํ™˜๋˜๋ฉฐ, ๋ณ€๋ถ„์ถ”๋ก  (variational inference)์„ ํ†ตํ•˜์—ฌ ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ํ›ˆ๋ จ์ด ์ง„ํ–‰๋œ๋‹ค. ํ›ˆ๋ จ์ด ๋๋‚œ ํ›„ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์œ ํšจ์„ฑ์„ ์ธก์ •ํ•˜์—ฌ ๋ถˆํ•„์š”ํ•œ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ์†Œ๊ฑฐํ•˜๋Š” ์‚ฌํ›„ ๋ชจ๋ธ ์••์ถ• ๋ฐฉ๋ฒ•์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๋ฐ˜๋„์ฒด ๊ณต์ •์— ๋Œ€ํ•œ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋Š” ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๊ณต์ •์˜ ๋ณต์žกํ•œ ๊ฑฐ๋™์„ ํšจ๊ณผ์ ์œผ๋กœ ๋ชจ๋ธ๋ง ํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ชจ๋ธ์˜ ์ตœ์  ๊ตฌ์กฐ๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ถ„ํฌํ˜• ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•œ ๊ฐ•ํ™”ํ•™์Šต์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ํ™•๋ฅ ์  ๊ณต์ • ์„ค๊ณ„ ํ”„๋ ˆ์ž„์›Œํฌ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ตœ์ ์น˜๋ฅผ ์ฐพ๊ธฐ ์œ„ํ•ด ์žฌ๊ท€์ ์œผ๋กœ ๋ชฉ์  ํ•จ์ˆ˜ ๊ฐ’์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ธฐ์กด์˜ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•๋ก ๊ณผ ๋‹ฌ๋ฆฌ, ๋ชฉ์  ํ•จ์ˆ˜ ๊ณก๋ฉด (objective function surface)์„ ๋งค๊ฐœํ™” ๋œ ํ™•๋ฅ ๋ถ„ํฌ๋กœ ๊ทผ์‚ฌํ•˜๋Š” ์ ‘๊ทผ๋ฒ•์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ด์‚ฐํ™” (discretization)๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ์—ฐ์†์  ํ–‰๋™ ์ •์ฑ…์„ ํ•™์Šตํ•˜๋ฉฐ, ํ™•์‹ค์„ฑ (certainty)์— ๊ธฐ๋ฐ˜ํ•œ ํƒ์ƒ‰ (exploration) ๋ฐ ํ™œ์šฉ (exploi-tation) ๋น„์œจ์˜ ์ œ์–ด๊ฐ€ ํšจ์œจ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ์‚ฌ๋ก€ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๊ณต์ •์˜ ์„ค๊ณ„์— ๋Œ€ํ•œ ๊ฒฝํ—˜์ง€์‹ (heuristic)์„ ํ•™์Šตํ•˜๊ณ  ์œ ์‚ฌํ•œ ์„ค๊ณ„ ๋ฌธ์ œ์˜ ํ•ด๋ฅผ ๊ตฌํ•˜๋Š” ๋ฐ ์ด์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค.Chapter 1 Introduction 1 1.1. Motivation 1 1.2. Outline of the thesis 5 Chapter 2 Backgrounds and preliminaries 9 2.1. Bayesian inference 9 2.2. Monte Carlo 10 2.3. Kullback-Leibler divergence 11 2.4. Variational inference 12 2.5. Riemannian manifold 13 2.6. Finite extended-pseudo-metric space 16 2.7. Reinforcement learning 16 2.8. Directed graph 19 Chapter 3 Process monitoring and fault classification with probabilistic manifold learning 20 3.1. Introduction 20 3.2. Methods 25 3.2.1. Uniform manifold approximation 27 3.2.2. Clusterization 28 3.2.3. Projection 31 3.2.4. Mapping of unknown data query 32 3.2.5. Inference 33 3.3. Verification study 38 3.3.1. Dataset description 38 3.3.2. Experimental setup 40 3.3.3. Process monitoring 43 3.3.4. Projection characteristics 47 3.3.5. Fault diagnosis 50 3.3.6. Computational Aspects 56 Chapter 4 Process system modeling with Bayesian neural networks 59 4.1. Introduction 59 4.2. Methods 63 4.2.1. Long Short-Term Memory (LSTM) 63 4.2.2. Bayesian LSTM (BLSTM) 66 4.3. Verification study 68 4.3.1. System description 68 4.3.2. Estimation of the plasma variables 71 4.3.3. Dataset description 72 4.3.4. Experimental setup 72 4.3.5. Weight regularization during training 78 4.3.6. Modeling complex behaviors of the system 80 4.3.7. Uncertainty quantification and model compression 85 Chapter 5 Process design based on reinforcement learning with distributional actor-critic networks 89 5.1. Introduction 89 5.2. Methods 93 5.2.1. Flowsheet hashing 93 5.2.2. Behavioral cloning 99 5.2.3. Neural Monte Carlo tree search (N-MCTS) 100 5.2.4. Distributional actor-critic networks (DACN) 105 5.2.5. Action masking 110 5.3. Verification study 110 5.3.1. System description 110 5.3.2. Experimental setup 111 5.3.3. Result and discussions 115 Chapter 6 Concluding remarks 120 6.1. Summary of the contributions 120 6.2. Future works 122 Appendix 125 A.1. Proof of Lemma 1 125 A.2. Performance indices for dimension reduction 127 A.3. Model equations for process units 130 Bibliography 132 ์ดˆ ๋ก 149๋ฐ•
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