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60th Anniversary Symposium of the International-Association-for-Shell-and-Spatial-Structures (IASS SYMPOSIUM) / 9th International Conference on Textile Composites and Inflatable Structures (STRUCTURAL MEMBRANES)

作者： Parigi, Dario Damkilde, Lars Aalborg Univ Dept Civil Engn Thomas Mann Vej 23 DK-9220 Aalborg Denmark

ISBN: (纸本)9788412110104

The constructive dimension of structures becomes more important today as the question of sustainability encompasses a broader scope in regards to material use. The inclusion of construction constraints within the digital design process enable novel design approaches, such as design and construction based on reuse of reclaimed material. Reciprocal structures were used in the past for different purposes, but in this context, it is worth noting their use for solving the practical issue of spanning distances especiallly in slabs when the available elements were shorter than the span. The use of reciprocally connected elements allows defining a set of variables that allows adapting to design constraints and goals related to structure and construction. Computational strategies are investigated to generate layouts that are able to span planar configurations with non-standard reclaimed elements. This work presents an optimization study for the design of standardized structural layouts for floors and/or walls, using the SPEA-II multi objective method. A set of geometrical and material parameters are defined for modelling the panel architecture, while competing objective functions are defined related primarily to aspects such as structural lightness and efficiency in construction. The results are presented in the form of Pareto optimal sets, from which conclusions can be drawn for the design of cost effective panels from reclaimed elements.

关键词： reuse upcycling reciprocal structures sustainability tectonics construction optimization timber structures sustainability

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Energy-efficient QoS-based OCDMA networks aided by nonlinear programming methods

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2019年 98卷 144-155页

作者： Pendeza Martinez, Cristiane A. Durand, Fabio Renan Abrao, Taufik Univ Tecnol Fed Parana Campus Cornelio Procopio BR-86300000 Cornelio Procopio PR Brazil Univ Estadual Londrina Dept Elect Engn Rod Celso Garcia Cid PR445POB 10-011 BR-86057970 Londrina PR Brazil

One of the advantages of the optical code division multiple access (OCDMA) system networks over other multiple access methods is the ability to provide differentiated classes of quality of service (QoS) for different users. The performance of the OCDMA networks is mainly limited by the multi-access interference (MAI) coming from other users sharing the same spectral resources. The suitable resource allocation in OCDMA networks allows greater capacity and the reliable deployment of the available resources in the network. This work aiming at applying and analyzing four analytical optimization methods applied to energy efficiency (EE) maximization of OCDMA networks. Hence, such optimization methods were applied and their performance-complexity tradeoffs are compared, namely the augmented Lagrangian method (ALM) (Hadi and Pakravan, 2018), sequential quadratic programming method (SQP), majoration-minimization (MaMi) approach (Sun et al., 2017;Stoica, 2004), as well as Dinkelbach's method (DK). Tests were performed considering practical and realistic OCDMA networks with a wide range of nodes, typically K = {8;16: 32} nodes. The evaluated optimization methods were able to achieve convergence and perform suitably to solve the optical EE-OCDMA optimization problem. However, in a few specific high loading system configurations, the DK method did not reach full convergence;besides DK processing time was much greater compared to the promising ALM, SQP and MaMi EE-OCDMA optimization approaches. (C) 2018 Elsevier GmbH. All rights reserved.

关键词： Energy efficiency OCDMA networks Augmented Lagrangian method Sequential quadratic programming method Majoration-minimization Dinkelbach's fractional optimization method CvX

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Data-Driven multi-objective Controller optimization for a Magnetically-Levitated Nanopositioning System

arXiv

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arXiv 2020年

作者： Li, Xiaocong Zhu, Haiyue Ma, Jun Teo, Tat Joo Teo, Chek Sing Tomizuka, Masayoshi Lee, Tong Heng Mechatronics Group Singapore Institute of Manufacturing Technology Singapore138634 Singapore Department of Mechanical Engineering University of California BerkeleyCA94720 United States Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

—The performance achieved with traditional model-based control system design approaches typically relies heavily upon accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task;and the usually necessary practical approximations often renders the automation system to operate in a non-optimal condition. This problem can be greatly aggravated in the case of a multi-axis magnetically-levitated (maglev) nanopositioning system where the fully floating behavior and multi-axis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under non-optimal conditions. These motion data essentially contain rich information;therefore, the possibility exists to develop an intelligent automation system to learn from these motion data, and to drive the system to operate towards optimality in a data-driven manner. Along this line then, this paper proposes a data-driven model-free controller optimization approach that learns from the past non-optimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multi-objective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data;the multi-objective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. In the work here, experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available. Copyright © 2020, The Authors. All rights reserved

关键词： Cost functions

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MFRL: A model-free reinforcement learning model for energy storage in microgrid systems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 288卷

作者： Tang, Chao Qin, Yunchuan Wu, Fan Tang, Zhuo State Grid Hunan Elect Power Co Ltd Informat & Commun Branch Changsha 410029 Hunan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

With the rapid development of renewable energy and the rise of microgrid systems, the role of energy storage technology in these systems is becoming increasingly important. Traditional energy storage control approaches often require the establishment of complex physical models. However, due to the complexity and uncertainty of microgrid systems, these models often encounter numerous difficulties and inaccuracies, which limit the performance and stability of the systems. In this paper, we propose a novel model-free reinforcement learning (MFRL) framework for optimizing energy storage strategies in microgrid systems, distinguishing itself by integrating multiple key stakeholders, including energy providers and end-users, into the system model. Different from existing studies that usually focus on simplified or single-agent models, our approach considers the interactions and different priorities of all relevant parties including energy suppliers and end-users, thus providing a more comprehensive system model. Based on this, we design the MFRL algorithm by combining the agent-based reinforcement with optimization strategies. MRFL autonomously learns the power procurement and consumption behavior of microgrid systems using Monte Carlo methods, and obtains the optimal power procurement and storage strategies. In addition, we further design a dynamic decision-making mechanism to enhance the adaptability of the system, enabling it to respond to changing operating conditions in real time. This mechanism can quickly adapt to changes in the microgrid operating environment across different scales. We conduct extensive simulations and practical experiments to verify the effectiveness of MRFL in adapting to dynamic operating conditions. The experimental results indicate that MRFL outperforms the state-of-the-art methods in terms of average return, providing a sustainable and efficient solution for optimizing energy storage in microgrids.

关键词： Energy storage Microgrid systems Model-free reinforcement learning multi-objective optimization

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Engineering optimization Theory and Practice, Fifth Edition 5th ed.

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2019年

作者： Singiresu S Rao

ISBN: (数字)9781119454816

ISBN: (纸本)9781119454717

The revised and updated new edition of the popular optimization book for engineers The thoroughly revised and updated fifth edition of Engineering optimization: Theory and Practice offers engineers a guide to the important optimization methods that are commonly used in a wide range of industries. The author—a noted expert on the topic—presents both the classical and most recent optimizations approaches. The book introduces the basic methods and includes information on more advanced principles and applications. The fifth edition presents four new chapters: Solution of optimization Problems Using MATLAB; Metaheuristic optimization Methods; multi-objective optimization Methods; and practical Implementation of optimization. All of the book's topics are designed to be self-contained units with the concepts described in detail with derivations presented. The author puts the emphasis on computational aspects of optimization and includes design examples and problems representing different areas of engineering. Comprehensive in scope, the book contains solved examples, review questions and problems. This important book: Offers an updated edition of the classic work on optimization Includes approaches that are appropriate for all branches of engineering Contains numerous practical design and engineering examples Offers more than 140 illustrative examples, 500 plus references in the literature of engineering optimization, and more than 500 review questions and answers Demonstrates the use of MATLAB for solving different types of optimization problems using different techniques Written for students across all engineering disciplines, the revised edition of Engineering optimization: Theory and Practice is the comprehensive book that covers the new and recent methods of optimization and reviews the principles and applications.

关键词： Engineering Mathematical optimization. Mathematical models.

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Energy-Aware Management in multi-UAV Deployments: Modelling and Strategies

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SENSORS 2020年第10期20卷 2791页

作者： Sanchez-Aguero, Victor Valera, Francisco Vidal, Ivan Tipantuna, Christian Hesselbach, Xavier IMDEA Networks Inst Avda Mar Mediterraneo 22 Madrid 28918 Spain Univ Carlos III Madrid Dept Ingn Telemat Avda Univ 30 Madrid 28911 Spain Univ Politecn Cataluna Dept Network Engn Calle Jordi Girona 1-3 E-08034 Barcelona Spain Escuela Politec Nacl Avda Ladron Guevara E11-253 Quito Ecuador

Nowadays, Unmanned Aerial Vehicles (UAV) are frequently present in the civilian environment. However, proper implementations of different solutions based on these aircraft still face important challenges. This article deals with multi-UAV systems, forming aerial networks, mainly employed to provide Internet connectivity and different network services to ground users. However, the mission duration (hours) is longer than the limited UAVs' battery life-time (minutes). This paper introduces the UAV replacement procedure as a way to guarantee ground users' connectivity over time. This article also formulates the practical UAV replacements problem in moderately large multi-UAV swarms and proves it to be an NP-hard problem in which an optimal solution has exponential complexity. In this regard, the main objective of this article is to evaluate the suitability of heuristic approaches for different scenarios. This paper proposes betweenness centrality heuristic algorithm (BETA), a graph theory-based heuristic algorithm. BETA not only generates solutions close to the optimal (even with 99% similarity to the exact result) but also improves two ground-truth solutions, especially in low-resource scenarios.

关键词： UAV UAV fleet UAV swarm energy consumption self-organization algorithms optimization UAV replacement

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Novel Policy Seeking with Constrained optimization

arXiv

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arXiv 2020年

作者： Sun, Hao Peng, Zhenghao Dai, Bo Lin, Dahua Zhou, Bolei Guo, Jian DAMTP Cambridge United Kingdom CS UCLA United States Shanghai AI Lab China IE CUHK Hong Kong

In problem-solving, we humans tend to come up with different novel solutions to the same problem. However, conventional reinforcement learning algorithms ignore such a feat and only aim at producing a set of monotonous policies that maximize the cumulative reward. The resulting policies usually lack diversity and novelty. In this work, we aim at enabling the learning algorithms with the capacity of solving the task with multiple solutions through a practical novel policy generation workflow that can generate a set of diverse and well-performing policies. Specifically, we begin by introducing a new metric to evaluate the difference between policies. On top of this well-defined novelty metric, we propose to rethink the novelty-seeking problem through the lens of constrained optimization, to address the dilemma between the task performance and the behavioral novelty in existing multi-objective optimization approaches, we then propose a practical novel policy-seeking algorithm, Interior Policy Differentiation (IPD), which is derived from the interior point method commonly known in the constrained optimization literature. Experimental comparisons on benchmark environments show IPD can achieve a substantial improvement over previous novelty-seeking methods in terms of both novelties of generated policies and their performances in the primal task. Copyright © 2020, The Authors. All rights reserved.

关键词： Constrained optimization

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Image Steganography Based on Artificial Immune in Mobile Edge Computing With Internet of Things

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IEEE ACCESS 2020年 8卷 136186-136197页

作者： Ding, Xuyang Xie, Ying Li, Pengxiao Cui, Mengtian Chen, Jianying Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Southwest Minzu Univ State Ethn Affairs Commiss Key Lab Comp Syst Chengdu 610041 Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing 100029 Peoples R China

Mobile edge computing provides high computing power, data storage capacity and bandwidth requirements for Internet of Things (IoT) through edge servers that process data close to data sources or users. In practical, mobile edge computing can be used to implement image steganography in IoT. Considering imperceptibility, security and capacity are important indicators for image steganography, this paper propose an image steganography based on evolutionary multi-objective optimization (EMOsteg). The EMOsteg preprocesses the image through a high-pass filters bank to find noise and texture regions that are difficult to model. By perturbing the image on noise and texture regions in multiple directions, the embedded capacity is increased. By defining the imperceptibility and security as an antigen, defining the perturbation positions of the cover image as an antibody, the EMOsteg uses the artificial immune principle to heuristically obtain the perturbation population through feature extraction of the perturbation and adaptive evolution operations. And the Pareto optimal is used to find the optimal perturbation in the last generation population. The simulation experiments analyze the convergence of the algorithm and the diversity of the solutions. In simulation experiments, the MSE, PSNR and SSIM were adopted to evaluate the imperceptibility, and the results show that the MSE value of our algorithm is 0.000308, the PSNR is 82.7501 and the SSIM approaches 1, they are better than comparison algorithms. The average detection error P-E under SPA was adopted to detect the security, and the results show that our algorithm is more robust against anti-SPA steganalysis. In order to evaluate the performance of real-time, the embedding time of the same secret under different algorithms were compared, and the results show that our algorithm is faster than comparison algorithms in the terminal. In summary, the proposed algorithm can maintain the image quality while resist steganalysis to

关键词： IoT security mobile edge computing image steganography evolutionary multi-objective optimization artificial immune

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Application of multi-objective optimization techniques for improved emissions and fuel economy over transient manoeuvres

Application of Multi-Objective optimization techniques for i...

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SAE World Congress Experience, WCX 2019

作者： Le Corre, Samuel David Mason, Byron Steffen, Thomas Winward, Edward Yang, Zhijia Childs, Thomas Cary, Mark Lygoe, Robert Loughborough University United Kingdom Ford Motor Co Ltd United Kingdom

This paper presents a novel approach to augment existing engine calibrations to deliver improved engine performance during a transient, through the application of multi-objective optimization techniques to the calibration of the Variable Valve Timing (VVT) system of a 1.0 litre gasoline engine. Current mature calibration approaches for the VVT system are predominantly based on steady state techniques which fail to consider the engine dynamic behaviour in real world driving, which is heavily transient. In this study the total integrated fuel consumption and engine-out NOx emissions over a 2-minute segment of the transient Worldwide Light-duty Test Cycle are minimised in a constrained multi-objective optimisation framework to achieve an updated calibration for the VVT control. The cycle segment was identified as an area with high NOx emissions. The optimisation framework was developed around a Mean Value Engine Model (MVEM) with representative engine controls which was validated against an engine tested on a dynamometer. The aim of this study was to demonstrate a practical benefit without having to significantly change the existing engine control strategy. Offline optimization with the MVEM model allows exploitation of workstation computational performance to effectively explore the calibration space, reducing both time and investment in engine testing. The initial simulation optimization results show a strong dominance of both fuel and NOx objectives with a potential reduction in fuel consumption and engine out NOX emissions of up to 5% and 18% respectively compared to the original steady-state based VVT calibration. Engine experimental results have confirmed that NOX emissions can be significantly reduced without any significant detriment to fuel economy over this 2min transient. © 2019 SAE International;Ford Motor Company. All rights reserved.

关键词： Fuel economy

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Ecosystem restoration in Europe: Can analogies to Traditional Chinese Medicine facilitate the cross-policy harmonization on managing socioecological systems?

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 657卷 1553-1567页

作者： Gocheva, Kremena Lu, Yihe Li, Feng Bratanova-Doncheva, Svetla Chipev, Nesho Bulgarian Acad Sci Inst Biodivers & Ecosyst Res Nr 2 Yuri Gagarin St BU-1113 Sofia Bulgaria Chinese Acad Sci State Key Lab Urban & Reg Ecol 18 Shuangqing Rd Beijing Peoples R China

Eli's Biodiversity Strategy to 2020 sets a 15% restoration target. However, the understanding of restoration as a management tool remains ambiguous at EU and Member State levels. As a country with rich biodiversity but low GDP, a well-defined priority setting approach is key for Bulgaria. The "Methodological framework for assessment and mapping of ecosystem condition and ecosystem services in Bulgaria" proposes a transition towards ecosystem management and monitoring of the Socio-Ecological System (SES), to be embedded in the environmental policy framework. We extend the analogy between SES and the human body's system in the Traditional Chinese Medicine (TCM) as a way to inform restoration priority setting and development of restoration and monitoring tools at several levels: 1. Overall objective: the sustainability goals correspond to the TCM's objective of a healthy, balanced body 2, Restoration objectives and functioning: the desired ecosystem state can be better understood in analogy between the ecosystem structure and functioning framework for multiparametric optimization, and the TCM framework of Ying-Yang, Eight principles, Five elements and organs 3. Ecosystem monitoring: Integrated monitoring may benefit from comparison to TCM diagnostics 4. Ecosystem management approaches can benefit from the analogy to the TCM system of meridians We apply the analogy and find that spatially explicit decision making on restoration, streamlined ecosystem monitoring and a number of other issues (green infrastructure, designation of protected areas, defragmentation and connectivity, cumulative impact assessment, etc.), are easier to understand, communicate, account for and manage. Ecosystem restoration is priority for China and the country has accumulated research and practical experience, including study of links between ecosystem management and the historical principles of Chinese philosophy. The Bulgarian and European approach to ecosystem based management can benefit from

关键词： Socio-ecological system Ecosystem based management and monitoring Traditional Chinese Medicine Ecosystem restoration targets Trade-offs multi parametric optimization of ecosystem services production

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