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arXiv 2023年

作者： El Housni, Omar Udwani, Rajan School of Operations Research and Information Engineering Cornell Tech Cornell University United States Departnent of Industrial Engineering and Operations Research UC Berkeley United States

Strategic product placement can have a strong influence on customer purchase behavior in physical stores as well as online platforms. Motivated by this, we consider the problem of optimizing the placement of substitutable products in designated display locations to maximize the expected revenue of the seller. We model the customer behavior as a two-stage process: first, the customer visits a subset of display locations according to a browsing distribution;second, the customer chooses at most one product from the displayed products at those locations according to a choice model. Our goal is to design a general algorithm that can select and place the products optimally for any browsing distribution and choice model, and we call this the Placement problem. We give a randomized algorithm that utilizes an α-approximate algorithm for cardinality constrained assortment optimization and outputs a logΘ(αm) -approximate solution (in expectation) for Placement with m display locations, i.e., our algorithm outputs a solution with value at least logΩ(αm) factor of the optimal and this is tight in the worst case. We also give algorithms with stronger guarantees in some special cases. In particular, we give an efficient deterministic logΩ(1)m -approximation algorithm for the Markov choice model, and a tight (1 − 1/e)-approximation algorithm for the problem when products have identical prices. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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Mitigation of climate change in Mediterranean existing social dwellings through numerical optimization of building stock models

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ENERGY AND BUILDINGS 2022年 266卷 1页

作者： Calama-Gonzalez, Carmen Maria Suarez, Rafael Leon-Rodriguez, Angel Luis Univ Seville Inst Univ Arquitectura & Ciencias Construcc Escuela Tecn Super Arquitectura Ave Reina Mercedes 2 Seville 41012 Spain

Retrofitting the existing building stock is widely accepted as a crucial factor to reaching 2030 and 2050 climate and energy targets, given that the building sector is among the top three most dominant energy consumers. This paper presents a bottom-up study which uses calibrated and parameterized energy stock models (building archetypes), while also incorporating building stock information from a large database. The thermal performance of the existing social housing stock of southern Spain is assessed through dynamic simulation under present and future climate change scenarios. Subsequently, several passive and low-cost operation-related strategies are numerically optimized through genetic algorithms to determine the best retrofit solutions, taking into consideration global warming scenarios. A multi-objective decision analysis is carried out by optimizing annual overheating hours (%), annual undercooling hours (%), and investment costs ( euro /m(2)). Among the conclusions reported, it is important to note the feasibility of implementing low-cost retrofit strategies considering investment costs of up to around 200 euro /m(2), which would lead to average annual overheating and undercooling hours below 55 % and 45 %, respectively. However, retrofit solutions exclusively based on passive and low-cost operation measures were proven to be significantly limited to improve thermal comfort results in the social stock. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： Social housing stock Building stock modelling Multi-objective analysis Sensitivity analysis Bottom-up optimization algorithms

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WELL PATH DESIGN USING Q-LEARNING algorithms AND BEZIER CURVES WITH OBSTACLES AVOIDANCE 41

WELL PATH DESIGN USING Q-LEARNING ALGORITHMS AND BEZIER CURV...

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ASME 41st International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Cao, Jie Chowdhury, Samiul Ehsan Wiktorski, Tomasz Sui, Dan Univ Stavanger Fac Sci & Technol Dept Energy & Petr Engn Stavanger Norway Univ Stavanger Fac Sci & Technol Dept Elect Engn & Comp Sci Stavanger Norway

ISBN: (纸本)9780791885956

Designing an optimal path has been considered as the one of the key challenges for drilling engineers. Even for a group of competent engineers, it takes several months to plan a well. A robust optimized path can influence the total cost of wellbore, wellbore quality and stability, transport efficiency, and drilling speed. A properly optimized path can be called if it is with a shortest path, avoids well-collisions, low touristy, and has maximum contact with the reservoir. Optimal design of wellbore trajectory is very demanding due to many complex and interacted drilling variables, models' uncertainties, and design constraints. In drilling engineering, well path optimization is typically based on the objectives to minimize of the total length of wellbore, drilling cost and time in consideration of well-collision avoidance. optimization algorithms such as genetic algorithm, dynamic programming, particle swarm optimization etc. might provide probable solutions to the optimization problem. Recently, machine learning technology becomes more attractive in drilling engineering to deal with complicated cases, like regression, classification, control and optimization problems. The aim of this paper is to investigate the implementation feasibility and efficiency of Reinforcement Learning algorithms to design an optimal path with the obstacle detection and avoidance. Our work has applied Q-Learning of Reinforcement Learning to plan a path with minimum measure depth, and in the meanwhile to avoid different shapes of obstacles. The agent interacts with the environment, achieving maximum reward upon reaching the target with passing through the given obstacles or receiving penalties when it fails. This work shows the behavior of the algorithms to obey the main criterion of the trajectory design as an alternative solution. In addition, one engineering path plan method based on Bezier curve to modify the generated path from Q-Learning is proposed, where the minimum dog leg severity

关键词： algorithms Design Drilling Trajectories (Physics) Reinforcement learning optimization Collisions (Physics) Engineers Reservoirs Stability Petroleum engineering Engineering design Dynamic programming Genetic algorithms optimization algorithms Particle swarm optimization Shapes Uncertainty Dynamic light scattering Machine learning

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Comparison of Deep Neural Network Learning algorithms for Biomedical Image Processing 5

Comparison of Deep Neural Network Learning Algorithms for Bi...

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5th International Conference on Informatics and Data-Driven Medicine (IDDM)

作者： Berezsky, Oleh Liashchynskyi, Petro Pitsun, Oleh Liashchynskyi, Pavlo Berezkyy, Mykola West Ukrainian Natl Univ 11 Lvivska St UA-46009 Ternopol Ukraine

In recent years, the popularity of deep neural networks used for various problem-solving tasks has increased dramatically. The main tasks include image classification and synthesis using convolutional and generative-adversarial neural networks. These types of networks need large amounts of training data to achieve the required accuracy and performance. In addition, these networks have a long training time. The authors of the paper analyzed and compared the gradient-based neural network learning algorithms. The biomedical image classification with the use of a convolutional neural network of a given architecture was carried out. A comparison of learning algorithms (SGD, Adadelta, RMSProp, Adam, Adamax, Adagrad, and Nadam) was made according to the following parameters: training time, training loss, training accuracy, test loss, and test accuracy. For the experiments, the authors used the Python programming language, the Keras machine learning library, and the Google Colaboratory development environment, which provides free use of the Nvidia Tesla K80 graphics processor. For the experiments tracking and logging the authors used the Weights & Biases service.

关键词： Machine learning CNN GAN optimization algorithms biomedical images

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A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10479页

作者： Kassab, Fadi Agha Rodriguez, Rusber Celik, Berk Locment, Fabrice Sechilariu, Manuela Univ Technol Compiegne Ctr Pierre Guillaumat CS 60203 F-60319 Compiegne France

This article comprehensively reviews strategies for optimal microgrid planning, focusing on integrating renewable energy sources. The study explores heuristic, mathematical, and hybrid methods for microgrid sizing and optimization-based energy management approaches, addressing the need for detailed energy planning and seamless integration between these stages. Key findings emphasize the importance of optimal sizing to minimize costs and reduce carbon dioxide (CO2) emissions while ensuring system reliability. In a pedagogical manner, this review highlights the integrated methodologies that simultaneously address sizing and energy management and the potential of emerging technologies, such as smart grids and electric vehicles, to enhance energy efficiency and sustainability. This study outlines the importance of accurate load modeling and carefully selecting models for renewable energy sources and energy storage systems, including degradation models, to achieve long-term operational efficiency and sustainability in microgrid design and operation. Future research should focus on developing multi-objective optimization techniques and incorporating cutting-edge technologies for improved microgrid planning and operation.

关键词： microgrid renewable energy integration energy management optimal sizing hybrid methodologies optimization algorithms sustainability smart grids

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Advanced Optical Imaging Based on Metasurfaces

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ADVANCED OPTICAL MATERIALS 2024年第6期12卷

作者： Zou, Xiujuan Lin, Ruoyu Fu, Yunlai Gong, Guangxing Zhou, Xuxi Wang, Shuming Zhu, Shining Wang, Zhenlin Nanjing Univ Sch Phys Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Key Lab Intelligent Opt Sensing & Manipulat Minist Educ Nanjing 210093 Peoples R China Collaborat Innovat Ctr Adv Microstruct Nanjing 210093 Peoples R China

Recently, metasurfaces have arisen as next-generation optics with various merits, such as being ultrathin and lightweight with multi-function integration. Numerous applications of metasurfaces have been demonstrated up to now, including wavefront shaping, optical imaging, hologram display, structured light generation, polarization detection, and so on. Among them, optical imaging components and techniques have been substantially promoted with the advent of metasurfaces due to their superior spatial modulation properties over electromagnetic waves. Here, the forward and reverse design approaches of metasurface and recent advances in various imaging applications based on them are reviewed. Due to the ability of arbitrary wavefront encoding, metasurfaces have been widely applied to numerous imaging applications, including diffraction-limited imaging with aberration correction, polarization-related imaging, high-resolution spectral imaging, three-dimentional (3D) imaging, and even algorithm-assisted imaging, etc. Optical imaging applications combined with novel metasurfaces can make breakthroughs with the development of nanofabrication technologies and the improvement of computer algorithms.

关键词： imaging applications metasurfaces optimization algorithms phase-modulation

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Deterministic and Stochastic Accelerated Gradient Method for Convex Semi-Infinite optimization

arXiv

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arXiv 2023年

作者： Yao, Yao Lin, Qihang Yang, Tianbao University of Iowa Iowa CityIA United States Texa A&M University College StationTX United States

This paper explores numerical methods for solving a convex differentiable semi-infinite program. We introduce a primal-dual gradient method which performs three updates iteratively: a momentum gradient ascend step to update the constraint parameters, a momentum gradient ascend step to update the dual variables, and a gradient descend step to update the primal variables. Our approach also extends to scenarios where gradients and function values are accessible solely through stochastic oracles. This method extends the recent primal-dual methods, for example, [25, 7], for optimization with a finite number of constraints. We show the iteration complexity of the proposed method for finding an ϵ-optimal solution under different convexity and concavity assumptions on the functions. © 2023, CC BY.

关键词： optimization algorithms

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NEW CONCEPT FOR DESIGN IN TURBOMACHINERY APPLICATIONS USING FULL RANS GRADIENT METHODOLOGY

NEW CONCEPT FOR DESIGN IN TURBOMACHINERY APPLICATIONS USING ...

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67th ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) on Road Mapping the Future of Propulsion and Power

作者： Del Rio, Armando Casartelli, Ernesto Fleischli, Benno Mangani, Luca Lucerne Univ Appl Sci & Arts Dept Mech Engn Tech Str 21 CH-6048 Horw Switzerland

ISBN: (纸本)9780791886113

Centrifugal compressors are mainly designed with the best efficiency point in mind and the focus oriented towards the impeller. The vaned diffuser geometry is usually developed using simple shapes, such as prismatic blades or wedges, which are unable to account for the flow distortion at the impeller outlet. Investigations of diffuser modifications using CFD during the design phase can help to extend the operating range. In order to assess the effect of such modifications, simulations in off-design conditions need to be performed. Accurate predictions of local flow structures at such conditions require a robust flow solver with mixing-plane interfaces and advanced turbulence modelling capabilities. In recent years diffuser modifications were performed mainly based on manual trial-and-error approaches and show considerable potential for improvements. To accelerate the process and allow automation, which is crucial in industrial applications, it is suggested to use an adjoint-based optimization algorithm for diffuser shape design. In this work a discrete adjoint solver implemented into a robust pressure-based CFD framework was used. While many optimization studies follow the frozen-turbulence approach, which essentially ignores the dependency of the sensitivity on the turbulence quantities, the present adjoint solver fully includes such effects. Automatic differentiation even allows for flexibility with respect to the choice of turbulence model: Any model available in the primal solver can also be used in the adjoint solver. In this particular case the k-epsilon EWT model was used, since it shows good convergence behavior and is capable of predicting the machine characteristic even at off-design conditions. Mesh morphing was realized via the Free Form Deformation method, which provides stable mesh deformation and offers a simple and fast set-up procedure. Several optimizations were conducted for the RWTH Aachen "Radiver" centrifugal compressor case with vaned diffuser

关键词： Design Reynolds-averaged Navier–Stokes equations Turbomachinery Diffusers Turbulence Flow (Dynamics) Deformation Machinery Compressors Computational fluid dynamics Impellers Shapes optimization Blades Errors Geometry optimization algorithms Engineering simulation Modeling Pressure Simulation Wedges Shape design

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An advanced hybrid deep learning model for predicting total dissolved solids and electrical conductivity (EC) in coastal aquifers

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ENVIRONMENTAL SCIENCES EUROPE 2024年第1期36卷 20-20页

作者： Jamshidzadeh, Zahra Latif, Sarmad Dashti Ehteram, Mohammad Khozani, Zohreh Sheikh Ahmed, Ali Najah Sherif, Mohsen El-Shafie, Ahmed Univ Kashan Dept Civil Engn Kashan Iran Soran Univ Sci Res Ctr Erbil Kurdistan Regio Iraq Komar Univ Sci & Technol Coll Engn Civil Engn Dept Sulaimany Kurdistan Regio Iraq Semnan Univ Dept Water Engn Semnan Iran Alfred Wegener Inst Paleoclimate Dynam Grp Helmholtz Ctr Polar & Marine Res D-27570 Bremerhaven Germany Sunway Univ Sch Engn & Technol Petaling Jaya 47500 Malaysia United Arab Emirates Univ Civil & Environm Engn Dept Coll Engn Al Ain 15551 U Arab Emirates Univ Malaya UM Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia

For more than one billion people living in coastal regions, coastal aquifers provide a water resource. In coastal regions, monitoring water quality is an important issue for policymakers. Many studies mentioned that most of the conventional models were not accurate for predicting total dissolved solids (TDS) and electrical conductivity (EC) in coastal aquifers. Therefore, it is crucial to develop an accurate model for forecasting TDS and EC as two main parameters for water quality. Hence, in this study, a new hybrid deep learning model is presented based on Convolutional Neural Networks (CNNE), Long Short-Term Memory Neural Networks (LOST), and Gaussian Process Regression (GPRE) models. The objective of this study will contribute to the sustainable development goal (SDG) 6 of the united nation program which aims to guarantee universal access to clean water and proper sanitation. The new model can obtain point and interval predictions simultaneously. Additionally, features of data points can be extracted automatically. In the first step, the CNNE model automatically extracted features. Afterward, the outputs of CNNE were flattened. The LOST used flattened arrays for the point prediction. Finally, the outputs of the GPRE model receives the outputs of the LOST model to obtain the interval prediction. The model parameters were adjusted using the rat swarm optimization algorithm (ROSA). This study used PH, Ca + + , Mg2 + , Na + , K + , HCO3, SO4, and Cl- to predict EC and TDS in a coastal aquifer. For predicting EC, the CNNE-LOST-GPRE, LOST-GPRE, CNNE-GPRE, CNNE-LOST, LOST, and CNNE models achieved NSE values of 0.96, 0.95, 0.92, 0.91, 0.90, and 0.87, respectively. Sodium adsorption ratio, EC, magnesium hazard ratio, sodium percentage, and total hardness indices were used to evaluate the quality of GWL. These indices indicated poor groundwater quality in the aquifer. This study shows that the CNNE-LOST-GPRE is a reliable model for predicting complex phenomena. Therefore,

关键词： Water quality Sustainable development goal (SDG) Deep learning optimization algorithms Point prediction Interval prediction

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Prediction of one- and three-months yoga practices effect on chronic venous insufficiency based on machine learning classifiers

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EGYPTIAN INFORMATICS JOURNAL 2024年 27卷

作者： Han, Xue Hu, Nan Weifang Univ Coll Phys Educ Weifang 261061 Shandong Peoples R China Baicheng Normal Univ Coll Phys Educ Baicheng 137000 Jilin Peoples R China

The rise of technology has heightened work demands, adversely impacting mental health and fitness. The COVID-19 pandemic exacerbates psychological stress, emphasizing the need for non-pharmacological interventions like yoga. Yoga positively influences the autonomic nervous system, benefiting cardio-respiratory health, metabolic efficiency, and conditions like Type-2 diabetes, Chronic Venous disease, and obesity. This study employs a dataset with 100 samples and 43 features related to Chronic Venous Insufficiency (CVI). Logistic and Random Forest classifiers are validated using K-fold cross-validation, with feature selection optimizing prediction accuracy. Hybrid models, enhanced with optimization algorithms, predict Venous Clinical Severity Score (VCSS) before, one, and three months after yoga practices. The Random Forest classifier, particularly RFGT, proves highly accurate in categorizing baseline severity and identifying Mild and Moderate CVI cases. RFGT demonstrated AUC score of 0.9072, 0.8714, 0.7709, and 0.7200 in Absent, Mild, Moderate, and Severe patient groups classification before yoga practices (VCSS-Pre). These values were 0.9158, 0.8644, 0.8142, and 0.6333 for VCSS-1 and reported as 0.9269, 0.8399, 0.7838, and 0.7500 for patients' classification in VCSS-3. Predicting VCSS scores before yoga intervention assists in categorizing participants for personalized care and efficient resource allocation. The RFC-based models, notably RFGT, show high accuracy in identifying baseline severity, enabling early intervention for high-risk individuals. These models, especially RFGT, perform well in classifying Mild and Moderate CVI cases, informing lifestyle modifications. Predicting VCSS-1 scores evaluates the short-term impact of yoga practices, identifying individuals requiring additional support. RFGT aids in personalized recommendations based on specific factors, crucial for severe conditions. Predicting VCSS-3 scores assesses the sustained impact over three month

关键词： Chronic venous insufficiency Cross validation Univariate selection Correlation matrix Classification methods optimization algorithms

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