Hasil untuk "Architectural drawing and design"

Pawan Kumar, Hokeun Kim

Precision agriculture promises higher yields and sustainability, but adoption is slowed by the high cost of cyber-physical systems (CPS) and the lack of systematic design methods. We present a cost-aware design space exploration (DSE) framework for multimodal drone-rover platforms to integrate budget, energy, sensing, payload, computation, and communication constraints. Using integer linear programming (ILP) with SAT-based verification, our approach trades off among cost, coverage, and payload while ensuring constraint compliance and a multitude of alternatives. We conduct case studies on smaller and larger-sized farms to show that our method consistently achieves full coverage within budget while maximizing payload efficiency, outperforming state-of-the-art CPS DSE approaches.

Fan Yang, Jiansong Zhang, Hazar Nicholas

Jiaxue Zhang, Qianying Lu, Ming Wu et al.

Stretchable displays that combine light-emitting capabilities with mechanical compliance are essential building blocks of next-generation wearable electronics. However, their widespread applications are currently limited by complex device architecture, limited pixel density, and immature fabrication processes. In this study, we present the device design and material developments of intrinsically stretchable light-emitting drawing displays that can show arbitrary hand-drawing features. The alternating-current electroluminescent display uses a simplified architecture comprising coplanar interdigitated liquid metal electrodes, an electroluminescent layer, and a dielectric encapsulation layer. Ink patterns on the device are coupled with the interdigitated electrodes under alternating voltage stimulations, generating localized electric fields for bright emissions. Various inks are prepared for painting, stamping, and stencil printing. Arbitrary luminous features on the devices can be either long-lasting or transient in characteristics. These skin-like devices are made entirely of compliant materials that can withstand bending, twisting, and stretching manipulations. Due to the excellent mechanical deformability, the drawing displays can be conformally laminated on the skin as body-integrated optoelectronic communication devices for graphic information.

Nadereh Afzhool, Ayten Özsavaş Akçay

This study investigates how trauma-informed and healing-centred architectural design is associated with rehabilitation and reintegration outcomes in young people’s correctional facilities. Drawing on international case studies, the analysis demonstrates that architecture is not a neutral backdrop but a contributing determinant within broader justice ecosystems. Trauma-informed environments are consistently linked to reductions in re-traumatisation and improvements in emotional regulation, while small-scale, community-oriented facilities are associated with enhanced skill development, autonomy, and reintegration potential. Culturally responsive designs that incorporate Indigenous practices and symbolic architecture are observed to support identity, resilience, and community belonging, underscoring the importance of cultural continuity in rehabilitation processes. In parallel, sustainable features such as biophilic design, renewable energy systems, and natural light are correlated with improvements in ecological performance and psychosocial well-being, indicating that sustainability and rehabilitation may be mutually reinforcing goals. Notably, the analysis highlights that supportive environments are also associated with staff well-being and institutional stability, underscoring the broader organisational benefits of healing architecture. The findings suggest that young people’s correctional facilities should not replicate adult prisons but instead provide safe, developmental, and culturally grounded spaces that respond to adolescents’ unique needs. This study contributes a novel conceptual model—the Trauma-Informed Healing Architecture (TIHA) framework—that integrates trauma-informed, cultural, and ecological design strategies within the Sustainable Development Goals (SDGs). The framework defines global standards as universal principles—safety, dignity, cultural responsiveness, and natural light—while remaining adaptable to local resources and justice systems. In this way, it provides internationally relevant yet context-sensitive guidance for young people’s correctional reform.

Weize Liu, Yongchi Zhao, Yijia Luo et al.

Large language models (LLMs) perform strongly on many language tasks but still struggle with complex multi-step reasoning across disciplines. Existing reasoning datasets often lack disciplinary breadth, reasoning depth, and diversity, as well as guiding principles for question synthesis. We propose DESIGNER: a DESIGN-logic-guidEd Reasoning data synthesis pipeline that leverages naturally available, extensive raw documents to generate multidisciplinary questions. The central insight is the notion of Design Logic, a form of reusable meta-knowledge that encapsulates the structured process human experts use to transform knowledge into complex exam questions, enabling LLMs to generate new questions with the same complex reasoning patterns from entirely different source texts with explicit control over difficulty, diversity, and question types. We use LLMs to reverse-engineer and abstract over 120,000 Design Logics from existing questions across various disciplines. By designing a two-stage retrieve-and-generate mechanism to match these Design Logics with raw corpus, we synthesized two large-scale reasoning datasets that span 75 disciplines: DLR-Book (3.04 million questions from the book corpus) and DLR-Web (1.66 million questions from the web corpus). Data analysis indicates that the questions synthesized by our method exhibit greater difficulty and diversity compared to those in the baseline datasets. Supervised fine-tuning (SFT) on Qwen3 and Llama3 with our data substantially improves multidisciplinary reasoning and outperforms baseline datasets. Notably, by applying SFT on the base versions of these models using only our data, we even surpass their official final models that have undergone the full post-training.

Yoon-jeong Shin

ABSTRACT This paper aims to investigate the existing design strategies of adaptive reuse and compare the strategies with the significant case, Tate Modern, which was converted from a power plant into a museum through competition. It analyses the winning architect’s strategies compared to other participating architects’ and how they made the conversion successful. This paper uses quantitative and qualitative research methods to analyze the strategies by comparing the competition brief, the minutes of the assessors, the drawings of design proposals, and interviews. The research reveals that the winning architect better understood the potential of the original building and employed a strategy of amplifying it by adding the least architectural components and giving overall uniformity to the old and new elements instead of contrasting them. They made the space integrating and multi-layered, not a dry background nor a distracting space but a challenging space for new art. This result indicates that adaptive reuse has to begin with an in-depth analysis of the existing building, its potential, and stakeholder needs, unlike constructing a new one. In addition, the participating architects’ strategies went beyond the scope of the existing theories’ classification, which shows that existing theories should be supplemented with future research and revised.

Soohyung Choi, Young-chae Kim, Taesik Nam et al.

Alejandro Veliz Reyes

Sai Krishna Revanth Vuruma, Ashley Margetts, Jianhai Su et al.

Generative Artificial Intelligence (AI) has shown tremendous prospects in all aspects of technology, including design. However, due to its heavy demand on resources, it is usually trained on large computing infrastructure and often made available as a cloud-based service. In this position paper, we consider the potential, challenges, and promising approaches for generative AI for design on the edge, i.e., in resource-constrained settings where memory, compute, energy (battery) and network connectivity may be limited. Adapting generative AI for such settings involves overcoming significant hurdles, primarily in how to streamline complex models to function efficiently in low-resource environments. This necessitates innovative approaches in model compression, efficient algorithmic design, and perhaps even leveraging edge computing. The objective is to harness the power of generative AI in creating bespoke solutions for design problems, such as medical interventions, farm equipment maintenance, and educational material design, tailored to the unique constraints and needs of remote areas. These efforts could democratize access to advanced technology and foster sustainable development, ensuring universal accessibility and environmental consideration of AI-driven design benefits.

Nikiforos Paraskevopoulos, David Hamel, Aritra Sarkar et al.

In the fast-paced field of quantum computing, identifying the architectural characteristics that will enable quantum processors to achieve high performance across a diverse range of quantum algorithms continues to pose a significant challenge. Given the extensive and costly nature of experimentally testing different designs, this paper introduces the first Design Space Exploration (DSE) for quantum-dot spin-qubit architectures. Utilizing the upgraded SpinQ compilation framework, this study explores a substantial design space comprising 29,312 spin-qubit-based architectures and applies an innovative optimization tool, ArtA (Artificial Architect), to speed up the design space traversal. ArtA can leverage 17 optimization configurations, significantly reducing exploration times by up to 99.1% compared to a traditional brute-force approach while maintaining the same result quality. After a comprehensive evaluation of best-matching optimization configurations per quantum circuit, ArtA suggests specific as well as universal architectural features that provide optimal performance across the examined circuits. Our work demonstrates that combining DSE methodologies with optimization algorithms can be effectively used to generate meaningful design insights for quantum processor development.

Elias Perdomo, Alexander Kropotov, Francelly Cano et al.

Emulating chip functionality before silicon production is crucial, especially with the increasing prevalence of RISC-V-based designs. FPGAs are promising candidates for such purposes due to their high-speed and reconfigurable architecture. In this paper, we introduce our Makinote, an FPGA-based Cluster platform, hosted at Barcelona Supercomputing Center (BSC-CNS), which is composed of a large number of FPGAs (in total 96 AMD/Xilinx Alveo U55c) to emulate massive size RTL designs (up to 750M ASIC cells). In addition, we introduce our FPGA shell as a powerful tool to facilitate the utilization of such a large FPGA cluster with minimal effort needed by the designers. The proposed FPGA shell provides an easy-to-use interface for the RTL developers to rapidly port such design into several FPGAs by automatically connecting to the necessary ports, e.g., PCIe Gen4, DRAM (DDR4 and HBM), ETH10g/100g. Moreover, specific drivers for exploiting RISC-V based architectures are provided within the set of tools associated with the FPGA shell. We release the tool online for further extensions. We validate the efficiency of our hardware platform (i.e., FPGA cluster) and the software tool (i.e., FPGA Shell) by emulating a RISC-V processor and experimenting HPC Challenge application running on 32 FPGAs. Our results demonstrate that the performance improves by 8 times over the single-FPGA case.

Haoyun Qin, Jian Lin, Hanyuan Liu et al.

Assistive drawing aims to facilitate the creative process by providing intelligent guidance to artists. Existing solutions often fail to effectively model intricate stroke details or adequately address the temporal aspects of drawing. We introduce hyperstroke, a novel stroke representation designed to capture precise fine stroke details, including RGB appearance and alpha-channel opacity. Using a Vector Quantization approach, hyperstroke learns compact tokenized representations of strokes from real-life drawing videos of artistic drawing. With hyperstroke, we propose to model assistive drawing via a transformer-based architecture, to enable intuitive and user-friendly drawing applications, which are experimented in our exploratory evaluation.

Berre Su Yanlic, Aykut Coskun

Design practice traditionally focused on human concerns, either overseeing the various effects of climate issues on nonhuman stakeholders or considering them as resources to address these problems. The climate crisis's urgency demands a design shift towards sustainability and inclusivity. This shift was happening through an emerging theme in design, More-Than-Human (MTH), which expands the notion of the user to animals, things, nature, and microbes. Such an expansion creates a requirement for designers to consider nonhuman perspectives during the design process. This paper investigates the methods used in MTH Design studies to explore and synthesize the perspectives of nonhuman users. Reviewing 30 papers, it highlights a predominant focus on animals and things over plants and microbes in MTH studies, along with a scarcity of synthesis methods. It identifies the necessity of tools that represent nonhumans with their relationships within larger ecosystems, and calls for increased attention to plants and microbes, emphasizing their vital role in sustainable environments and urging researchers to develop methods for understanding these species. By highlighting method strengths and weaknesses, it aims to guide designers and design researchers who plan to work with nonhuman users in selecting appropriate methods.

Yu Cao, Xiangqiao Meng, P. Y. Mok et al.

It is a time-consuming and tedious work for manually colorizing anime line drawing images, which is an essential stage in cartoon animation creation pipeline. Reference-based line drawing colorization is a challenging task that relies on the precise cross-domain long-range dependency modelling between the line drawing and reference image. Existing learning methods still utilize generative adversarial networks (GANs) as one key module of their model architecture. In this paper, we propose a novel method called AnimeDiffusion using diffusion models that performs anime face line drawing colorization automatically. To the best of our knowledge, this is the first diffusion model tailored for anime content creation. In order to solve the huge training consumption problem of diffusion models, we design a hybrid training strategy, first pre-training a diffusion model with classifier-free guidance and then fine-tuning it with image reconstruction guidance. We find that with a few iterations of fine-tuning, the model shows wonderful colorization performance, as illustrated in Fig. 1. For training AnimeDiffusion, we conduct an anime face line drawing colorization benchmark dataset, which contains 31696 training data and 579 testing data. We hope this dataset can fill the gap of no available high resolution anime face dataset for colorization method evaluation. Through multiple quantitative metrics evaluated on our dataset and a user study, we demonstrate AnimeDiffusion outperforms state-of-the-art GANs-based models for anime face line drawing colorization. We also collaborate with professional artists to test and apply our AnimeDiffusion for their creation work. We release our code on https://github.com/xq-meng/AnimeDiffusion.

Hao Zheng, Philip F. Yuan

Abstract Machine learning, as a computational tool for finding mappings between the input and output data, has been widely used in engineering fields. Researchers have applied machine learning models to generate 2D drawings with pixels or 3D models with voxels, but the pixelization reduces the precision of the geometries. Therefore, in order to learn and generate 3D geometries as vectorized models with higher precision and faster computation speed, we develop a specific artificial neural network, learning and generating design features for the forms of buildings. A customized data structure with feature parameters is constructed, meeting the requirements of the neural network by rebuilding surfaces with controlling points and appending additional input neurons as quantified vectors to describe the properties of the design. The neural network is first trained with generated design data and then tested by adjusting the feature parameters. The prediction of the generated data shows the basic generative ability of the neural network. Furthermore, trained with design data collected from existing buildings, the neural network learns and infers the geometric design features of architectural design with different feature parameters, providing a data-driven method for designers to generate and analyze architectural forms.

Emma Buchanan, G. Loporcaro, S. Lukosch

This article reports on a study that assessed whether Virtual Reality (VR) can be used to display Building Information Modelling (BIM) metadata alongside spatial data in a virtual environment, and by doing so determine if it increases the effectiveness of the design review by improving participants’ understanding of the design. Previous research has illustrated the potential for VR to enhance design reviews, especially the ability to convey spatial information, but there has been limited research into how VR can convey additional BIM metadata. A user study with 17 healthcare professionals assessed participants’ performances and preferences for completing design reviews in VR or using a traditional design review system of PDF drawings and a 3D model. The VR condition had a higher task completion rate, a higher SUS score and generally faster completion times. VR increases the effectiveness of a design review conducted by healthcare professionals.

Letizia Bollini, Francesco E. Guida

In this second issue devoted to memory and its relationship with the digital, we have chosen the image of an object, a Micronesian navigation chart.2 It originates within a tradition of implicit and empirical knowledge of a territory made experiential and handed down through an actual artefact. Unlike an ancient portulanus (pilot book), the map is physical and tactile and does not realistically reproduce or represent the morphology of the territory but rather a model of it. However, like Western nautical cartographies, this orientation tool embeds different levels of knowledge of a territory, or rather, of a context within which it is necessary to orient oneself in order to interact. Oriented thanks to the stellar compass, they include quantitative and qualitative information on ocean currents, flows, winds and "betia" or time-varying environment-based seamarks.

Marina Rigillo

S. Rasoulzadeh, M. Wimmer, P. Stauss et al.

We present Strokes2Surface, an offline geometry reconstruction pipeline that recovers well-connected curve networks from imprecise 4D sketches to bridge concept design and digital modeling stages in architectural design. The input to our pipeline consists of 3D strokes' polyline vertices and their timestamps as the 4th dimension, along with additional metadata recorded throughout sketching. Inspired by architectural sketching practices, our pipeline combines a classifier and two clustering models to achieve its goal. First, with a set of extracted hand-engineered features from the sketch, the classifier recognizes the type of individual strokes between those depicting boundaries (Shape strokes) and those depicting enclosed areas (Scribble strokes). Next, the two clustering models parse strokes of each type into distinct groups, each representing an individual edge or face of the intended architectural object. Curve networks are then formed through topology recovery of consolidated Shape clusters and surfaced using Scribble clusters guiding the cycle discovery. Our evaluation is threefold: We confirm the usability of the Strokes2Surface pipeline in architectural design use cases via a user study, we validate our choice of features via statistical analysis and ablation studies on our collected dataset, and we compare our outputs against a range of reconstructions computed using alternative methods.

Ziyue Liu, Yixing Li, Jing Hu et al.

Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural network-based thermal prediction models can perform real-time prediction for many unseen new designs. However, existing works either solve 2D temperature fields only or do not generalize well to new designs with unseen design configurations (e.g., heat sources and boundary conditions). In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations. This framework learns a functional map from the function space of multiple key PDE configurations (e.g., boundary conditions, power maps, heat transfer coefficients) to the function space of the corresponding solution (i.e., temperature fields), enabling fast thermal analysis and optimization by changing key design configurations (rather than just some parameters). We test DeepOHeat on some industrial design cases and compare it against Celsius 3D from Cadence Design Systems. Our results show that, for the unseen testing cases, a well-trained DeepOHeat can produce accurate results with $1000\times$ to $300000\times$ speedup.