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Optimization Method for Spatial Element Layout of Urban Landscape Architecture Based on generative algorithm

Procedia Computer Science 2025年 261卷 345-353页

作者： Di Zhu Xi’an FanYi University Xi’an 710105 Shaanxi China

In urban landscape design, the optimization of spatial layout is very important because it directly affects the functionality, ecological benefits and aesthetic effects of the landscape. This paper introduces a generative algorithm to optimize the layout of garden space elements. It aims to improve the rationality, aesthetics and functionality of the layout scheme through automation and intelligence, so as to better adapt to different environments and user needs. First, a geometric model of the urban landscape space is constructed, and the functional areas, green spaces, water bodies and other elements of the garden are converted into design constraints and objective functions suitable for generative algorithm processing. Then, using genetic algorithm as an optimization tool, through the iterative evolution process of the population, the position, form and combination of spatial elements are gradually adjusted to achieve the predetermined design goals. Finally, the fitness function is introduced to evaluate the pros and cons of each generation of layout schemes, including the openness of the landscape view, the coherence of the functional areas and the aesthetic evaluation. Through multiple iterations, the algorithm gradually converges to the optimal solution and provides a garden layout scheme that meets specific needs. In the layout optimization under complex terrain, the space utilization rate is between 75.0% and 77.8%. The landscape view is distributed between 66.3% and 68.0%, which is relatively evenly distributed. The urban landscape garden space element layout optimization method based on the generative algorithm introduced in this paper not only improves the design efficiency and accuracy but also provides new ideas for urban garden planning.

关键词： generative algorithm Urban Landscape Architecture Spatial Element Layout Optimization Landscape Vision

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Beyond Single-Protein de novo Design: A generative algorithm for the NTF2-like Superfamily

Beyond Single-Protein de novo Design: A Generative Algorithm...

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作者： Basanta, Benjamin University of Washington

学位级别：Ph.D.

Natural proteins evolved over billions of years to regulate cellular growth, ward off infection and capture and store solar energy. Proteins thus serve as the molecular basis for life. The promise of protein design is to use nature’s favorite toolbox to solve modern human problems without having to wait for the long and meandering path of natural selection. Protein structure determines function, so it is not surprising proteins sample a great variety of structures. Thus, it is reasonable to expect that designing proteins with functions not seen in nature would require access to comparable structural diversity, more specifically, diversity of active site structure. Despite the advances in de novo protein design, the systematic generation of proteins containing pockets that can harbor substrates has been lacking. The use of a natural small alpha-beta fold, the Nuclear Transport Factor 2-like (NTF2-like) fold, to design a high affinity small-molecule binding protein, and the diversity observed in that family, have posed the idea that significant pocket structural diversity could be derived from this relatively simple, small, alpha-beta fold. To explore this idea, we analyzed the structures of proteins belonging to the NTF2-like superfamily and other proteins with similar characteristics to understand the determinants of their structural diversity. The most salient feature of the NTF2-like superfamily is the curved beta-sheet that forms most of their pockets in its concave face. Curved beta sheets depart from the classic beta pleated structure displaying bulges, tight kinks and irregular bending and twisting. The first step towards de novo design of a large variety of NTF2-like proteins is devising principles for designing curved beta sheets. In this work, we demonstrate we can generate a number of different curved sheets in the context of NTF2-like proteins. We then use these principles, along with additional information from native NTF2-like proteins, to create a gene

关键词： Computational Biology generative algorithm generative design High-throughput screening Protein design Biochemistry Biological chemistry

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Modular coordination-based generative algorithm to optimize construction waste

Modular coordination-based generative algorithm to optimize ...

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International High-Performance Built Environment Conference (iHBE)

作者： Banihashemi, Saeed Tabadkani, Amir Hosseini, M. Reza Univ Technol Sydney Sch Built Environm 15 Broadway Ultimo 2007 Australia Politecn Milan Dept Bldg & Architectural Engn I-20133 Milan Italy Deakin Univ Sch Architecture & Built Environm Geelong Vic 3220 Australia

In response to the growing attention to sustainable built environment, this study aims at introducing an approach in construction waste optimization through integrating parametric design with offsite construction methodology. To this end, a generative algorithm was developed within the integrated platform of Rhino and Grasshopper software based on modular coordination rules and ASTM international standards as the design constraints in modules array. Two sets of horizontal and vertical modules were obtained from a prototype model while an evolutionary solver function was employed for optimizing the generated waste. This resulted in developing different modular design variants which generate the minimum amount of waste while being fully compliant with international standards. This study contributes to the field by presenting one of the first studies in its kind focusing on the integration of parametric design into offsite construction methodology through the lenses of construction waste optimization. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Offsite construction parametric design generative algorithm modular coordination waste optimization

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Modular Coordination-based generative algorithm to Optimize Construction Waste

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Procedia Engineering 2017年 180卷 631-639页

作者： Saeed Banihashemi Amir Tabadkani M. Reza Hosseini School of built environment University of Technology Sydney (UTS) 15 Broadway Ultimo 2007 Australia Department of building and architectural engineering Politecnico di Milano Milano 20133 Italy School of architecture & built environment Deakin University Geelong 3220 Australia

关键词： Offsite construction parametric design generative algorithm modular coordination waste optimization

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Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6538页

作者： Lee, Seokwon Chung, Wonzoo Korea Univ Dept Artificial Intelligence Seoul 02841 South Korea

In this paper, a novel deep learning approach based on conditional FLOW is proposed for sea clutter augmentation. Sea clutter data augmentation is important for testing detection algorithms for maritime remote sensing and surveillance due to the expensive and time-consuming nature of sea clutter data acquisition. Whereas the conventional parametric methods face challenges in finding appropriate distributions and modeling time correlations of sea clutter data, the proposed deep learning approach, GCC-FLOW, can learn the data distribution from the data without explicitly defining a mathematical model. Furthermore, unlike the existing generative deep learning approaches, the proposed GCC-FLOW is able to synthesize sea clutter data of arbitrary length with the stable autoregressive structure using conditional FLOW. In addition, the proposed algorithm generates sea clutter data not only with the same characteristics of the training data, but also with the interpolated characteristics of different training data by introducing a global condition variable corresponding to the target characteristic, such as sea state. Experimental results demonstrate the effectiveness of the proposed GCC-FLOW in generating sea clutter data of arbitrary length under different sea state conditions.

关键词： sea clutter data augmentation generative algorithm conditional flow

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Bi-Mode Inverse Design of 3D Structures for MEMS Resonators

IEEE ELECTRON DEVICE LETTERS

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IEEE ELECTRON DEVICE LETTERS 2025年第5期46卷 841-844页

作者： Zuo, Binzhou Wu, Zeyu Zhao, Junyuan Niu, Bo Lei, Yumo Cao, Lixin Zhu, Yinfang Yang, Jinling Inst Semicond Beijing 100083 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Inst Phys Beijing 100190 Peoples R China

This work presents an automated algorithm for MEMS resonator structure generation based on inverse design, integrating deep learning and neural networks to predict key physical properties, including resonance frequency (f), quality factor of thermoelastic damping (Q(TED)), and motional impedance (R-x). Unlike traditional methods relying on finite element analysis (FEA), this approach leverages a database-driven deep learning model, achieving prediction speeds 9,740 times faster than the conventional FEA software with an average accuracy of 97.5%, 96.5%, 96.4 for f, Q(TED) and R-x,respectively. The algorithm supports flexural and Lame modes and could generate resonators with a broad frequency range from similar to 8 to similar to 63 MHz, significantly surpassing existing methods. By efficiently predicting seed structures, the method guides the inverse design process, generating high Q, and low R-x resonator structures within 10 minutes. The generated devices exhibit deviations of less than 3% from target performance metrics. Simulations and experimental results validate the feasibility and effectiveness of the proposed algorithm, highlighting its potential for accelerating MEMS design with enhanced performance and precision.

关键词： Resonant frequency Genetic algorithms Deep learning Micromechanical devices Accuracy Training Three-dimensional displays Prediction algorithms Neural networks Generators MEMS resonator generative algorithm deep learning inverse design

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A Machine Learning generative Method for Automating Antenna Design and Optimization

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2022年 7卷 285-295页

作者： Zhong, Yang Renner, Peter Dou, Weiping Ye, Geng Zhu, Jiang Liu, Qing Huo Meta Real Lab Sunnyvale CA 94087 USA Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Meta Real Lab Sunnyvale NC 27708 USA

To facilitate the antenna design with the aid of computer, one of the practices in consumer electronic industry is to model and optimize antenna performances with a simplified antenna geometric scheme. The ease of handling multi-dimensional optimization problems and the less dependence on the engineers' knowledge and experience are the key to achieve the popularity of simulation-driven antenna design and optimization for the industry. In this paper, we introduce a flexible geometric scheme with the concept of mesh network that can form any arbitrary shape by connecting different nodes. For such problems with high dimensional parameters, we propose a machine learning based generative method to assist the searching of optimal solutions. It consists of discriminators and generators. The discriminators are used to predict the performance of geometric models, and the generators to create new candidates that will pass the discriminators. Moreover, an evolutionary criterion approach is proposed for further improving the efficiency of our method. Finally, not only optimal solutions can be found, but also the well trained generators can be used to automate future antenna design and optimization. For a dual resonance antenna design, our proposed method is better than the other mature algorithms.

关键词： Antennas Generators Optimization Computational modeling Measurement Geometric modeling Machine learning Antenna modeling antenna optimization machine learning generative algorithm evolutionary approach

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Experimental investigation and numerical modeling of oxidative pyrolysis mechanism of mining PVC cable sheath

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2022年第24期147卷 14479-14490页

作者： Zhao, Jinlong Ma, Hanchao Huang, Hong Zhang, Jianping Chen, Youliang China Univ Min & Technol State Key Lab Coal Resources & Safe Min Beijing Peoples R China China Univ Min & Technol Sch Emergency Management & Safety Engn Beijing Peoples R China Tsinghua Univ Inst Publ Safety Res Dept Engn Phys Beijing Peoples R China Ulster Univ Belfast Sch Architecture & Built Environm Newtownabbey North Ireland China Acad Safety Sci & Technol Beijing Peoples R China

Cables modified with flame retardants are widely used in the coal mining process to enhance their fire safety performance. This paper investigates experimentally and numerically the thermo-oxidative degradation process of PVC-based mining cable sheath, which consists of PVC resin and plasticizers (Phthalate esters), fillers (Calcium carbonate), and flame retardant (Antimony trioxide) as additives. Thermogravimetry coupled with Fourier transform infrared spectroscopy (TG-FTIR) experiments were performed at different heating rates in an air atmosphere. TG results showed that the decomposition processes of mining PVC cable sheath are more complicated than those of pure PVC with additional degradation steps, which was further confirmed by FTIR gas analysis. A deconvolution method was used to distinguish the independent reactions from the overlapped derivative thermogravimetric (DTG) peaks. It was found that the whole degradation process can be divided into seven steps, based on which an oxidative pyrolysis model was developed: pyrolysis of plasticizers (steps 1 and 2), dehydrogenation (step 3), emission and combustion of volatile fraction (step 4), carbon combustion (step 5), oxidative pyrolysis of complicated additives (step 6), and decomposition of residues (step 7). The kinetic triplets (activation energy, pre-exponential factor, and reaction model function) for each reaction were firstly calculated using three commonly used model-free methods and then further optimized using the genetic algorithm (GA). Based on the optimized parameters, the reaction mechanisms and their associated kinetic parameters were determined. The findings of this study are important in understanding the oxidative pyrolysis process of PVC cable sheath, and the obtained kinetic parameters can also be used for its pyrolysis and fire modeling, waste recycling, and risk assessment.

关键词： PVC cable sheath Oxidative pyrolysis Reaction kinetics Deconvolution generative algorithm TG-FTIR

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Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 166卷 107549-107549页

作者： Qu, Zhiguo Shi, Wenke Tiwari, Prayag Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Equipment Technol Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing 210044 Peoples R China Halmstad Univ Sch Informat Technol Halmstad Sweden

To address the scarcity and class imbalance of abnormal electrocardiogram (ECG) databases, which are crucial in AI-driven diagnostic tools for potential cardiovascular disease detection, this study proposes a novel quantum conditional generative adversarial algorithm (QCGAN-ECG) for generating abnormal ECG signals. The QCGAN-ECG constructs a quantum generator based on patch method. In this method, each sub-generator generates distinct features of abnormal heartbeats in different segments. This patch-based generative algorithm conserves quantum resources and makes QCGAN-ECG practical for near-term quantum devices. Additionally, QCGAN-ECG introduces quantum registers as control conditions. It encodes information about the types and probability distributions of abnormal heartbeats into quantum registers, rendering the entire generative process controllable. Simulation experiments on Pennylane demonstrated that the QCGAN-ECG could generate completely abnormal heartbeats with an average accuracy of 88.8%. Moreover, the QCGAN-ECG can accurately fit the probability distribution of various abnormal ECG data. In the anti-noise experiments, the QCGANECG showcased outstanding robustness across various levels of quantum noise interference. These results demonstrate the effectiveness and potential applicability of the QCGAN-ECG for generating abnormal ECG signals, which will further promote the development of AI-driven cardiac disease diagnosis systems. The source code is available at ***/VanSWK/QCGAN_ECG.

关键词： Quantum generative adversarial network Abnormal electrocardiogram generative algorithm Data imbalance

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Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2022年第40期119卷 e2209524119-e2209524119页

作者： Khare, Eesha Yu, Chi-Hua Obeso, Constancio Gonzalez Milazzo, Mario Kaplan, David L. Buehler, Markus J. MIT Lab Atomist & Mol Mech Cambridge MA 02139 USA MIT Dept Mat Sci & Engn Cambridge MA 02139 USA Natl Cheng Kung Univ Dept Engn Sci Tainan 701 Taiwan Tufts Univ Dept Biomed Engn Medford MA 02155 USA Univ Pisa Dept Civil & Ind Engn I-56122 Pisa Italy MIT Schwarzman Coll Comp Ctr Computat Sci & Engn Cambridge MA 02139 USA Ctr Mat Sci & Engn Cambridge MA 02139 USA

Collagen is the most abundant structural protein in humans, providing crucial mechan-ical properties, including high strength and toughness, in tissues. Collagen-based bio-materials are, therefore, used for tissue repair and regeneration. Utilizing collagen effectively during materials processing ex vivo and subsequent function in vivo requires stability over wide temperature ranges to avoid denaturation and loss of structure, mea-sured as melting temperature (T-m). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to Tm values, a robust framework to facilitate the design of collagen sequences with specific Tm remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific Tm values. We report 1,000 de novo collagen sequences, and we show that we can efficiently use this model to generate collagen sequences and verify their T-m values using both experimen-tal and computational methods. We find that the model accurately predicts T-m values within a few degrees centigrade. Further, using this model, we conduct a high -throughput study to identify the most frequently occurring collagen triplets that can be directly incorporated into collagen. We further discovered that the number of hydrogen bonds within collagen calculated with molecular dynamics (MD) is directly correlated to the experimental measurement of triple-helical quality. Ultimately, we see this work as a critical step to helping researchers develop collagen sequences with specific Tm values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm.

关键词： collagen deep learning thermal stability generative algorithm mechanics

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