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Joint optimization of ship scheduling and speed reduction: A new strategy considering high transport efficiency and low carbon of ships in port

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OCEAN ENGINEERING 2021年 233卷

作者： Xia, Zicheng Guo, Zijian Wang, Wenyuan Jiang, Ying Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China

Ship emissions is one of the main components of port pollution. Inefficient waterway management may not only result in additional waiting time of ships, but also the aggravated emissions in port. With special consideration of combining scheduling optimization and speed reduction of ships in port, this paper proposed a new emission reduction method, called ship scheduling with speed reduction (SSSR). Performance of the SSSR method has been testified through comparisons between the new and traditional ship emission reduction methods based on the navigation safety under three different strategies. The effectiveness of the new ship emission reduction method has been testified through a series of experiments with practical data in port. Especially, under various stages in port, the emissions of ships can be reduced by 8.0%-11.9% and the traffic efficiency can be improved by 3.8%-6.2%.

关键词： Emission reduction Ship scheduling Speed reduction optimization algorithm Port waterway

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New Approach in Antenna Design Automation Applied to a Dual-Band GNSS Micro-Array 15

New Approach in Antenna Design Automation Applied to a Dual-...

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15th European Conference on Antennas and Propagation (EuCAP)

作者： Rudant, Lionel Batel, Lotfi Clemente, Antonio Delaveaud, Christophe Univ Grenoble Alpes CEA Leti F-38000 Grenoble France

ISBN: (纸本)9788831299022

This article describes a new approach in antenna design automation. We propose to use a micro-array of coupled antennas loaded by impedances and to perform the optimization of these impedance values in order to reach specific design goals. In particular, we present a demonstration for dual-band GNSS that requires good RHCP gain (in the range -2 dBic to 0 dBic) and low LHCP gain (below -10 dBic). The proposed automated design process has been able to meet these requirements jointly in L2 (1215-1240 MHz) and L1 frequency bands (1560-1610 MHz). A prototype has been fabricated, and characterized in anechoic chamber to validate the proposed design methodology.

关键词： micro-array parasitic radiating elements antenna coupling flow diagram GNSS multi-band antenna design automation optimization algorithm

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Adaptive Learning Rate and Momentum for Training Deep Neural Networks 1

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21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Hao, Zhiyong Jiang, Yixuan Yu, Huihua Chiang, Hsiao-Dong Cornell Univ Ithaca NY 14850 USA

ISBN: (数字)9783030865238

ISBN: (纸本)9783030865238;9783030865221

Recent progress on deep learning relies heavily on the quality and efficiency of training algorithms. In this paper, we develop a fast training method motivated by the nonlinear Conjugate Gradient (CG) framework. We propose the Conjugate Gradient with Quadratic line-search (CGQ) method. On the one hand, a quadratic line-search determines the step size according to current loss landscape. On the other hand, the momentum factor is dynamically updated in computing the conjugate gradient parameter (like Polak-Ribiere). Theoretical results to ensure the convergence of our method in strong convex settings is developed. And experiments in image classification datasets show that our method yields faster convergence than other local solvers and has better generalization capability (test set accuracy). One major advantage of the paper method is that tedious hand tuning of hyperparameters like the learning rate and momentum is avoided.

关键词： optimization algorithm Line search Deep learning

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Predicting Wind Power Generation Using Hybrid Deep Learning With optimization

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2021年第8期31卷

作者： Hossain, Md Alamgir Chakrabortty, Ripon K. Elsawah, Sondoss Gray, Evan Mac A. Ryan, Michael J. Griffith Univ Queensland Micro & Nanotechnol Ctr Nathan Qld 4111 Australia Univ New South Wales Canberra Capabil Syst Ctr Sch Engn & Informat Technol Canberra ACT 2612 Australia

Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco Rock Wind Farm in Australia. The model consists of a convolutional neural network, gated recurrent units (GRU) and a fully connected neural network. To improve performance, the hyper-parameters of the model are tuned using the Harris Hawks optimization algorithm. The effectiveness of the proposedmodel is evaluated against other advanced models, including multilayer feedforward neural network (NN), recurrent neural network (RNN), long short-termmemory(LSTM) and GRU. The forecasting model demonstrates around 38% and 24% higher accuracy as compared to the 5- and 10-min forecasting of the NN model, respectively.

关键词： Very-short term prediction wind power generation hybrid deep learning optimization algorithm Boco Rock Wind Farm

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optimization of the MMF spatial harmonic content to design electrical machine winding

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2020年第1_SUPPL期64卷 S99-S114页

作者： Marault, Jerome Gillon, Frederic Hecquet, Michel Tounzi, Abdelmounaim Univ Lille Arts & Metiers Inst Technol Yncrea Haut France Cent LilleULR 2697L2EP F-59000 Lille France

The winding design of an electrical machine is an important task in the whole design process. This leads, for a part, to the magneto motive force which is one of the main quantities to manage for electrical machine performances. Indeed, the latter is directly linked to that of the air gap magnetic flux density and thus to the torque ripples, vibration and then noise. This paper proposes to reduce the MMF harmonic content by means of optimization process using mono-objective or multi-objective algorithms with discrete and continuous variables. For this aim, optimization algorithm is coupled with an analytical tool which enables calculating quickly the MMF harmonic content from winding parameters. A winding optimization of three different windings with the same number of pole pairs is proposed to show the suitability of this process.

关键词： MMF harmonic content mono-objective multi-objective optimization algorithm new winding pattern winding design

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Exergo-economic analysis and multi-objective multi-verse optimization of a solar/biomass-based trigeneration system using externally-fired gas turbine, organic Rankine cycle and absorption refrigeration cycle

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APPLIED THERMAL ENGINEERING 2021年 191卷

作者： Nazari, Navid Mousavi, Seyedmostafa Mirjalili, Seyedali Univ Melbourne Fac Engn & Informat Technol FEIT Dept Infrastruct Engn Renewable Energy & Energy Efficiency Grp Melbourne Vic 3010 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia

In the present work, an innovative configuration is proposed based on solar pre-heating concept and biomass direct-combustion for combined production of electricity, hot water and cooling load. The studied system consists of an externally fired gas turbine, a trans-critical organic Rankine cycle, and a Li-Br/H2O absorption refrigeration cycle. The proposed system is thoroughly analyzed from the energy, exergy, and exergo-economic viewpoint. The results of the exergo-economic analysis demonstrate that the energy and exergy efficiencies of the reference case are 55.56% and 20.38% and the product cost rate is 26.4 $/h. Comparing the thermodynamic results to the literature, it is proved that the proposed system generates a considerably higher amount of power, heating, and cooling load which results in an improvement of energy efficiency by about 10%. In further, the main design parameters of each cycle are parametrically assessed to gain a comprehensive understanding of the system behavior. Finally, a new multi-objective optimization algorithm called multi-objective multi-verse optimizer (MOMVO) is employed to maximize exergy efficiency and minimize the product cost rate of the system. Compared to the base case, TOPSIS implementation revealed that the optimal final solution of the Pareto-frontier found by the MOMVO has about 9% higher second law efficiency while the product cost rate is decreased by around 6%. This result proved the better performance of the MOMVO compared to other conventional evolutionary-based multi-objective optimzation algorithms.

关键词： Biomass Solar thermal Exergo-economic CCHP Multi-objective multi-verse optimizer (MOMVO) optimization algorithm

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Dynamic data driven sensor placement for enabling capability estimation of self - aware aerospace vehicles

Dynamic data driven sensor placement for enabling capability...

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AIAA Aviation Forum and Exposition

作者： Singh, Arjun Allaire, Douglas Texas A&M Univ J Mike Walker Dept Mech Engn 66 College Stn TX 77843 USA

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

Unmanned aerial vehicles (UAV) are utilized in numerous industries and in recent years with the advent of learning techniques, the focus is now on developing Self - aware UAVs that rely on an array of environmental sensors to replace a pilots awareness of the structural capability of the UAV and provide time critical analysis of the sensor data to make complex decisions in real time. Hence a self-aware UAV is capable of dynamically and autonomously sense its structural state and act accordingly to perform the required task. This work proposes a data driven approach to producing estimates of capability for a self - aware UAV and optimizing the placement of sensors to minimize the error between true and predicted capability. This process involves using high physics-based models such as ASWING in tandem with Akselos modeler, a cloud based FEA solver, to produce an offline library that comprises of damage states along with capabilities corresponding to different kinematic states of a UAV. Further this generated information is used to create a classification model which is used to predict the capability for real time data. The classification model serves as an enabler for the optimization algorithm to measure the error value between the true and the predicted capability of the UAV to determine the optimum sensor placement. We demonstrate the improvement in performance through a comparison between optimum placement and the standard placement of sensors. We also provide evidence of proof of concept of how dynamic sampling of information can improve the process of capability estimation in self - aware aerospace vehicles.

关键词： Sensor Technology Unmanned Aerial Vehicle optimization algorithm Genetic algorithm Naive Bayes Classifier Structural Health Monitoring Allowable Stress Structural Load Shear Strength Bayes Theorem

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Surrogated finite element models using machine learning

Surrogated finite element models using machine learning

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Chierichetti, Maria Davoudi, Fatemeh Huang, Daniel Vurturbadarinath, Poojitha San Jose State Univ San Jose CA 95051 USA San Jose State Univ Dept Aerosp Engineer San Jose CA 95051 USA San Jose State Univ Dept Aviat & Technol San Jose CA 95051 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. This problem is particularly relevant in the development of autonomous vehicles, especially in the concept of urban air mobility. The actual usage of the vehicle will be used to predict stresses in the structure and therefore to define maintenance scheduling. Machine learning algorithms, specifically the Random Forest algorithm, can be used to create surrogate finite element models that map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis model of the same system. As a result, the FEA-based machine learning approach directly estimates the dynamic response over the entire system during operations, thus improving the ability to define ad-hoc, safe and efficient maintenance procedures. The predictive performance of random forest algorithm is presented for direct estimation of the acceleration distribution over a beam structure.

关键词： Machine Learning Finite Element Analysis Dynamic Response Urban Air Mobility Beam (Structures) Numerical Modeling Stress Distribution optimization algorithm Open Source Software Young's Modulus

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Averting and Mitigating the Effects of Uncertainties with Optimal Control in Industrial Networked Control System 7

Averting and Mitigating the Effects of Uncertainties with Op...

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7th IEEE International Symposium on Smart Electronic Systems (IEEE-iSES)

作者： Solanki, Brijraj Singh Kumawat, Renu Srinivasan, Seshadhri Manipal Univ Jaipur Elect & Commun Engn Jaipur Rajasthan India Kalasalingam Acad Res & Educ Instrumentat & Control Engn Srivilliputhur Tamil Nadu India

ISBN: (纸本)9781728187532

Emerging trends, development and growth of industrial-networked control systems (i-NCSs) with real-time communication network drive it more susceptible to malicious intended intrusion and attack. Due to numerous advantages such as reduced maintenance, ease to install, ease of diagnosis and system wiring draws attention to use in various industrial and critical fields. The control performance and robust stability of NUS is directly related to reliable and successful transmission of critical information's. So in this paper an approach is illustrated to alleviate the effects and avert the unwanted intended intrusive data through the designing of stability conditions and optimized control policies. An unwanted intrusion effects also forced us to design a controlled system, which is hard to be estimated by attackers, through the applications of optimization algorithms.

关键词： Networked control system (NCS) denial of service (DoS) attacks optimal control optimization algorithm delays packer loss

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Design of a Dual Thrust Solid Motor using Star Grains

Design of a Dual Thrust Solid Motor using Star Grains

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： El-Naggar, Mostafa Belal, Hatem Abdalla, Hamed M. Egyptian Armed Forces Cairo Egypt Mil Tech Coll Cairo Egypt

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Dual thrust profile using single chamber offers great opportunity in extending range of tactical missiles by mitigating the drawbacks of other dual thrust motor arrangements. This method for achieving dual thrust profile can be implemented using different grains arrangement e.g. tubular-tubular, star tubular, star-end burning etc. or different propellant compositions. In this paper, Star-star configuration is presented. The advantage of the proposed arrangement is higher volumetric loading compared to other arrangement. In addition, effects of different geometric parameters on dual thrust profile are investigated. Finally Genetic algorithm optimization module is applied in order to fit the design into certain objective mission. Results showed that number of star points and star angular fraction affect only transition phase, while geometry of narrow star affect both booster and sustainer phase.

关键词： Dual Thrust Composite Solid Propellant Genetic algorithm Tactical Missiles Solid Rocket Motor Universal Gas Constant Burning Rate Space Shuttle Solid Rocket Booster optimization algorithm Stagnation Pressure

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