597 research outputs found
強化学習を用いた離散構造物の最適設計
京都大学新制・課程博士博士(工学)甲第23153号工博第4797号新制||工||1750(附属図書館)京都大学大学院工学研究科建築学専攻(主査)教授 大崎 純, 教授 竹脇 出, 准教授 倉田 真宏学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA
Reinforcement learning for optimum design of a plane frame under static loads
A new method is presented for optimum cross-sectional design of planar frame structures combining reinforcement learning (RL) and metaheuristics. The method starts from RL jointly using artificial neural network so that the action taker, or the agent, can choose a proper action on which members to be increased, reduced or kept their size. The size of the neural network is compressed into small numbers of inputs and outputs utilizing story-wise decomposition of the frame. The trained agent is used in the process of generating a neighborhood solution during optimization with simulated annealing (SA) and particle swarm optimization (PSO). Because the proposed method is able to explore the solution space efficiently, better optimal solutions can be found with less computational cost compared with those obtained solely by metaheuristics. Utilization of RL agent also leads to high-quality optimal solutions regardless of variation of parameters of SA and PSO or initial solution. Furthermore, once the agent is trained, it can be applied to optimization of other frames with different numbers of stories and spans
FDMopt: Force density method for optimal geometry and topology of trusses
This paper presents a new efficient tool for simultaneous optimization of topology and geometry of truss structures. Force density method is applied to formulate optimization problem to minimize compliance under constraint on total structural volume, and objective and constraint functions are expressed as explicit functions of force density only. This method does not need constraints on nodal locations to avoid coalescent nodes, and enables to generate optimal solutions with a variety in topology and geometry. Furthermore, for the purpose of controlling optimal shapes, tensor product Bézier surface is introduced as a design surface. The optimization problem is solved using sensitivity coefficients and the optimizer is compiled as a component compatible with Grasshopper, an algorithmic modeling plug-in for Rhinoceros, which is a popular 3D modeling software. Efficiency and accuracy of the proposed method are demonstrated through two numerical examples of semi-cylindrical and semi-spherical models
Reinforcement Learning and Graph Embedding for Binary Truss Topology Optimization Under Stress and Displacement Constraints
This paper addresses a combined method of reinforcement learning and graph embedding for binary topology optimization of trusses to minimize total structural volume under stress and displacement constraints. Although conventional deep learning methods owe their success to a convolutional neural network that is capable of capturing higher level latent information from pixels, the convolution is difficult to apply to discrete structures due to their irregular connectivity. Instead, a method based on graph embedding is proposed here to extract the features of bar members. This way, all the members have a feature vector with the same size representing their neighbor information such as connectivity and force flows from the loaded nodes to the supports. The features are used to implement reinforcement learning where an action taker called agent is trained to sequentially eliminate unnecessary members from Level-1 ground structure, where all neighboring nodes are connected by members. The trained agent is capable of finding sub-optimal solutions at a low computational cost, and it is reusable to other trusses with different geometry, topology, and boundary conditions
Graph-based reinforcement learning for discrete cross-section optimization of planar steel frames
A combined method of graph embedding (GE) and reinforcement learning (RL) is developed for discrete cross-section optimization of planar steel frames, in which the section size of each member is selected from a prescribed list of standard sections. The RL agent aims to minimize the total structural volume under various practical constraints. GE is a method for extracting features from data with irregular connectivity. While most of the existing GE methods aim at extracting node features, an improved GE formulation is developed for extracting features of edges associated with members in this study. Owing to the proposed GE operations, the agent is capable of grasping the structural property of columns and beams considering their connectivity in a frame with an arbitrary size as feature vectors of the same size. Using the feature vectors, the agent is trained to estimate the accurate return associated with each action and to take proper actions on which members to reduce or increase their size using an RL algorithm. The applicability of the proposed method is versatile because various frames different in the numbers of nodes and members can be used for both training and application phases. In the numerical examples, the trained agents outperform a particle swarm optimization method as a benchmark in terms of both computational cost and design quality for cross-sectional design changes; the agents successfully assign reasonable cross-sections considering the geometry, connectivity, and support and load conditions of the frames
<Poster Presentation 11>Noise-induced Phenomena in Two Strongly Pulse-coupled Spiking Neuron Models
[Date] November 28 (Mon) - December 2 (Fri), 2011: [Place] Kyoto University Clock Tower Centennial Hall, Kyoto, JAPA
Burst synchronization in two pulse-coupled resonate-and-fire neuron circuits
The present paper addresses burst synchronization in out of phase observed in two pulse-coupled resonate-and-fire neuron (RFN) circuits. The RFN circuit is a silicon spiking neuron that has second-order membrane dynamics and exhibits fast subthreshold oscillation of membrane potential. Due to such dynamics, the behavior of the RFN circuit is sensitive to the timing of stimuli. We investigated the effects of the sensitivity and the mutual interaction on the dynamic behavior of two pulse-coupled RFN circuits, and will demonstrate out of phase burst synchronization and bifurcation phenomena through circuit simulations.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI
Artwork Explanation in Large-scale Vision Language Models
Large-scale vision-language models (LVLMs) output text from images and
instructions, demonstrating advanced capabilities in text generation and
comprehension. However, it has not been clarified to what extent LVLMs
understand the knowledge necessary for explaining images, the complex
relationships between various pieces of knowledge, and how they integrate these
understandings into their explanations. To address this issue, we propose a new
task: the artwork explanation generation task, along with its evaluation
dataset and metric for quantitatively assessing the understanding and
utilization of knowledge about artworks. This task is apt for image description
based on the premise that LVLMs are expected to have pre-existing knowledge of
artworks, which are often subjects of wide recognition and documented
information. It consists of two parts: generating explanations from both images
and titles of artworks, and generating explanations using only images, thus
evaluating the LVLMs' language-based and vision-based knowledge. Alongside, we
release a training dataset for LVLMs to learn explanations that incorporate
knowledge about artworks. Our findings indicate that LVLMs not only struggle
with integrating language and visual information but also exhibit a more
pronounced limitation in acquiring knowledge from images alone. The datasets
(ExpArt=Explain Artworks) are available at
https://huggingface.co/datasets/naist-nlp/ExpArt
Relationships between seismic design conditions and superior design solutions of steel buildings
The Japanese version of this paper was published in volume 87, number 802, pages 1257–1266, https://doi.org/10.3130/aijs.87.1257, of the Journal of Structural and Construction Engineering (Transactions of AIJ). The authors have obtained permission from the editor of the Journal of Structural and Construction Engineering (Transactions of AIJ) for secondary publication of the English version in another journal. This paper is a translation of the original Japanese version with slight modifications and corrections.Superior design solutions (SDSs) of seven-story steel buildings are obtained for the space frame system (SFS) and the perimeter frame system (PFS) in various types of structures such as moment frames (MF) and those with buckling restrained braces (BRBF). The SDSs satisfy the design constraints of allowable stress design (ASD) and calculations of resistance and limit state (CRLS) with minimized steel volume. The influences of design conditions are comparatively evaluated. It is found that the maximum inter-story drift ratio for the safety limit in the CRLS for MF can be rationally defined as 2% and ASD can be more dominant for BRBF
Author Correction : Direct measurement of radiation exposure dose to individual organs during diagnostic computed tomography examination
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