Hasil untuk "Architectural drawing and design"

Xusheng Du, Athiwat Kongkaeo, Ye Zhang et al.

For architectural design, representation across multiple Levels of Details (LoD) is essential for achieving a smooth transition from conceptual massing to detailed modeling. However, traditional LoD modeling processes rely on manual operations that are time-consuming, labor-intensive, and prone to geometric inconsistencies. While the rapid advancement of generative artificial intelligence (AI) has opened new possibilities for generating multi-level architectural models from sketch inputs, its application remains limited by the lack of high-quality paired LoD training data. To address this issue, we propose an automatic LoD sketch extraction framework using generative AI models, which progressively simplifies high-detail architectural models to automatically generate geometrically consistent and hierarchically coherent multi-LoD representations. The proposed framework integrates computer vision techniques with generative AI methods to establish a progressive extraction pipeline that transitions from detailed representations to volumetric abstractions. Experimental results demonstrate that the method maintains strong geometric consistency across LoD levels, achieving SSIM values of 0.7319 and 0.7532 for the transitions from LoD3 to LoD2 and from LoD2 to LoD1, respectively, with corresponding normalized Hausdorff distances of 25.1% and 61.0% of the image diagonal, reflecting controlled geometric deviation during abstraction. These results verify that the proposed framework effectively preserves global structure while achieving progressive semantic simplification across different LoD levels, providing reliable data and technical support for AI-driven multi-level architectural generation and hierarchical modeling.

A. Telier

Brian Judge, Mark Nitzberg, Stuart J. Russell

This article examines the challenges of regulating artificial intelligence (AI) systems and proposes an adapted model of regulation suitable for AI’s novel features. Unlike past technologies, AI systems built using techniques like deep learning cannot be directly analyzed, specified, or audited against regulations. Their behavior emerges unpredictably from training rather than intentional design. However, the traditional model of delegating oversight to an expert agency, which has succeeded in high-risk sectors like aviation and nuclear power, should not be wholly discarded. Instead, policymakers must contain risks from today’s opaque models while supporting research into provably safe AI architectures. Drawing lessons from AI safety literature and past regulatory successes, effective AI governance will likely require consolidated authority, licensing regimes, mandated training data and modeling disclosures, formal verification of system behavior, and the capacity for rapid intervention.

Jozsef Balogh, Irene Parada, Gelasio Salazar

In a simple drawing of a graph every pair of edges intersect each other in at most one point, which is either a common endvertex or a proper crossing. For each positive integer $n$, Negami identified a drawing $B_n$ of the complete bipartite graph $K_{n,n}$, and proved that if $N$ is sufficiently large, then every drawing of $K_{N,N}$ contains a drawing of $K_{n,n}$ weakly isomorphic to $B_n$. Thus $B_n$ is (up to weak isomorphism) the only {\em unavoidable} drawing of $K_{n,n}$. We extend this result to complete multipartite graphs, characterizing their unavoidable drawings.

Houjiang Liu, Yiheng Su, Matthew Lease

We explore potential benefits of incorporating Rhetorical Design into the design of Explainable Artificial Intelligence (XAI) systems. While XAI is traditionally framed around explaining individual predictions or overall system behavior, explanations may also function as rhetorical arguments that shape how users evaluate a system's usefulness and credibility, and how they develop appropriate trust for adoption. In real-world, in-situ interactions, explanations can thus produce experiential and affective rhetorical effects that are not fully captured by traditional XAI design goals that focus primarily on how AI works. To address this gap, we propose Rhetorical XAI, which bridges two explanatory goals: how AI works and why AI merits use. Rhetorical XAI comprises three appeals in explanation design: logos, which aligns technical logic with human reasoning through visual and textual abstractions; ethos, which establishes contextual credibility based on the explanation source and its appropriateness to the decision task; and pathos, which engages user emotionally by framing explanations around their motivations, expectations, or situated needs during interaction. We conduct a narrative review synthesizing design strategies from prior XAI work aligned with these three rhetorical appeals, highlighting both opportunities and challenges of integrating rhetorical design into XAI.

Xinhui Ye, Joep Frens, Jun Hu

Exploration is crucial in the design process and is known for its essential role in fostering creativity and enhancing design outcomes. Within design teams, exploration evolves into co-exploration, a collaborative and dynamic practice that this study aims to unpack. To investigate this experience, we conducted a longitudinal observational study with 61 students across 16 design teams. Over five months of weekly diary-interviews, we uncovered the intricate dynamics of co-exploration. Our main contribution is a four-dimensional framework that identifies five distinct patterns of co-exploration activities. Our findings reveal how co-exploration emerges across various activities throughout the design process, demonstrating its role in different team interactions. It fosters a sense of togetherness, keeping design teams open-minded and engaged. This engagement cultivates collective intelligence, enabling teams to actively share knowledge, build upon each other's ideas, and achieve outcomes beyond individual contributions. Our study underscores the value of co-exploration, suggesting that it reflects the trajectory of design success and warrants further research. We also provide actionable insights, equipping future practitioners with strategies to enhance co-exploration in design collaborations.

Sarah Abbas, Nathalie Okdeh, Rabih Roufayel et al.

Simple Summary This literature review delves into the interdisciplinary field of neuroarchitecture, exploring the significant impact of architectural design on human behavior, emotions, and cognitive processes. It examines the roles of specific brain regions, such as the Anterior Cingulate Cortex (ACC) and the Parahippocampal Place Area (PPA), in perceiving and responding to architectural environments. The review also discusses the influence of mirror neurons in empathetic reactions to architecture, the emotional effects of design elements like natural light and color, and the importance of architectural features in spatial navigation and wayfinding. The paper aims to highlight the profound connection between architectural spaces and neurological functioning, emphasizing architecture’s role in enhancing human well-being. Abstract The study of neuroarchitecture is concerned with the significant effects of architecture on human behavior, emotions and thought processes. This review explores the intricate relationship between the brain and perceived environments, focusing on the roles of the anterior cingulate cortex (ACC) and parahippocampal place area (PPA) in processing architectural stimuli. It highlights the importance of mirror neurons in generating empathetic responses to our surroundings and discusses how architectural elements like lighting, color, and space layout significantly impact emotional and cognitive experiences. The review also presents insights into the concept of cognitive maps and spatial navigation, emphasizing the role of architecture in facilitating wayfinding and orientation. Additionally, it addresses how neuroarchitecture can be applied to enhance learning and healing environments, drawing upon principles from the Reggio Emilia approach and considerations for designing spaces for the elderly and those with cognitive impairments. Overall, this review offers a neuroscientific basis for understanding how human cognition, emotions, spatial navigation, and well-being are influenced by architectural design.

Xiao Zhang, Chongxing Song, Tao You et al.

Significant advancements have been made in hyperspectral image (HSI) super-resolution with the development of deep-learning techniques. However, the current application of deep neural network architectures to HSI super-resolution heavily relies on empirical design strategies, which can potentially impede the improvement of image reconstruction performance and introduce distortions in the results. To address this, we propose an innovative HSI super-resolution network called dual ordinary differential equations (Dual ODEs). Drawing inspiration from ordinary differential equations (ODEs), our approach offers reliable guidelines for the design of HSI super-resolution networks. The Dual ODE model leverages a spatial ODE block to extract spatial information and a spectral ODE block to capture internal spectral features. This is accomplished by redefining the conventional residual module using the multiple ODE functions method. To evaluate the performance of our model, we conducted extensive experiments on four benchmark HSI datasets. The results conclusively demonstrate the superiority of our Dual ODE approach over state-of-the-art models. Moreover, our approach incorporates a small number of parameters while maintaining an interpretable model design, thereby reducing model complexity.

Francesco Armato

Questo articolo esplora il ruolo del design come strumento di trasformazione dello spazio urbano, evidenziando come scenografie effimere e concrete possano favorire il senso di appartenenza e arricchire l'identità dei luoghi. Attraverso una rassegna critica di teorie e pratiche, si analizzano interventi di design partecipativo e performance urbane che reinterpretano la città come palcoscenico di vita quotidiana. Il testo discute il valore delle installazioni temporanee, come il Temporary Design e il Tactical Urbanism , nel generare nuove dinamiche sociali e culturali. Con il contributo di riferimenti teorici da autori come Norberg-Schulz, Jacobs e Bauman, e esempi iconici di arte pubblica, si propone una visione del design urbano come catalizzatore per la creazione di spazi espressivi, emozionali e inclusivi. L'articolo conclude riflettendo sull'importanza di preservare l'identità dei luoghi contro le pressioni della globalizzazione e del consumismo, promuovendo soluzioni sostenibili e sensibili alle esigenze delle comunità locali.

Hoon Kim, Minje Jang, Wonjun Yoon et al.

We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism.

Julia Katheder, Philipp Kindermann, Fabian Klute et al.

We introduce the $k$-Plane Insertion into Plane drawing ($k$-PIP) problem: given a plane drawing of a planar graph $G$ and a set $F$ of edges, insert the edges in $F$ into the drawing such that the resulting drawing is $k$-plane. In this paper, we show that the problem is NP-complete for every $k\ge 1$, even when $G$ is biconnected and the set $F$ of edges forms a matching or a path. On the positive side, we present a linear-time algorithm for the case that $k=1$ and $G$ is a triangulation.

Austin Cooper, Sean Meyn

The field of quickest change detection (QCD) concerns design and analysis of algorithms to estimate in real time the time at which an important event takes place, and identify properties of the post-change behavior. It is shown in this paper that approaches based on reinforcement learning (RL) can be adapted based on any "surrogate information state" that is adapted to the observations. Hence we are left to choose both the surrogate information state process and the algorithm. For the former, it is argued that there are many choices available, based on a rich theory of asymptotic statistics for QCD. Two approaches to RL design are considered: (i) Stochastic gradient descent based on an actor-critic formulation. Theory is largely complete for this approach: the algorithm is unbiased, and will converge to a local minimum. However, it is shown that variance of stochastic gradients can be very large, necessitating the need for commensurately long run times; (ii) Q-learning algorithms based on a version of the projected Bellman equation. It is shown that the algorithm is stable, in the sense of bounded sample paths, and that a solution to the projected Bellman equation exists under mild conditions. Numerical experiments illustrate these findings, and provide a roadmap for algorithm design in more general settings.

Michael Poli, Armin W Thomas, Eric Nguyen et al.

The development of deep learning architectures is a resource-demanding process, due to a vast design space, long prototyping times, and high compute costs associated with at-scale model training and evaluation. We set out to simplify this process by grounding it in an end-to-end mechanistic architecture design (MAD) pipeline, encompassing small-scale capability unit tests predictive of scaling laws. Through a suite of synthetic token manipulation tasks such as compression and recall, designed to probe capabilities, we identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis, training over 500 language models between 70M to 7B parameters. Surprisingly, we find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures via isolated proxy tasks. The new architectures found via MAD, based on simple ideas such as hybridization and sparsity, outperform state-of-the-art Transformer, convolutional, and recurrent architectures (Transformer++, Hyena, Mamba) in scaling, both at compute-optimal budgets and in overtrained regimes. Overall, these results provide evidence that performance on curated synthetic tasks can be predictive of scaling laws, and that an optimal architecture should leverage specialized layers via a hybrid topology.

Ferhat Pakdamar, Rana Uzun

Building occupants spend approximately 90% of their lives indoors where they want to have indoor air quality, visual, acoustic, and thermal comfort (which is more dominant). Thermal comfort is assessed by physical factors such as operative air temperature, relative humidity, and air velocity. People’s activity level and clothing level are also effective. Related regulations and standards like ISOEN7730 and EN15251 aim to provide a unified understanding of the matter. Since these studies rely on experimental methods, there are instances where certain scenarios lack experimental support, leading to gaps in the results. Those gaps can be filled with the Fuzzy Logic Method, which evaluates with “degrees of truth” instead of “true or false”. With this study, the level of knowledge on providing thermal comfort can be increased by filling the gaps in the empirical studies and the damage caused by heating-cooling energy to the environment can be reduced with further studies.

Fabio Parascandolo, Rossano Pazzagli, Daniela Poli

This article reads the technological management of complex and global events, including SARS-CoV-2 pandemic, as part of the transition of European democracies towards control and surveillance models imposed by normal emergencies following one another with increasing frequency. The text reflects on pandemics along history, on the technocratic and digital trajectory of contemporary societies, and concludes by outlining local-based forms of self-government.

Alessia Garozzo, Francesco Maggio

<p>This study, starting from the representations of a number of projects kept in Palermo in public and private archives, seeks to highlight their role not only in the knowledge of little-known architecture, but above all their value as custodians of memory and bearers of knowledge. “The architecture in the drawer”, that which remains on paper and not debased by compromises, not only takes on a “material” value but above all an ethical value because making visible the thought and operative practice of an architectural scholar is a moral act.<br />Analytical and critical redesign, in this sense, contributes with its natural slowness, which is synonymous with reflection, to nourishing knowledge of the project, of making architecture, of compositional and figurative processes and, last but not least, of the constituent principles of an architectural thought.<br />Through the analysis of a number of drawings preserved in two archives containing projects by Salvatore Caronia Roberti and Salvatore Cardella, the aim is to show part of the points of a timeline, in this case coinciding, describing themes and modes of architecture making in Palermo, with the awareness that many significant passages are left out.<br />Thus a drawing of a timeline, open and welcoming, which however identifies two substantial points in its development; two graphical stages that may constitute further reflections that assist architectural historiography and above all the knowledge of what has often been relegated to an unknown fate.</p><p>DOI: https://doi.org/10.20365/disegnarecon.31.2023.9 </p>

Sumita Sharma, Netta Iivari, Leena Ventä-Olkkonen et al.

Children increasingly use applications utilizing Artificial Intelligence / Machine Learning (AI/ML). Given the propensity of such applications to propagate existing social, gender, and racial biases, it becomes imperative to consider designing and developing child-centered AI applications for children. Furthermore, children should have opportunities and skills to critically reflect on current applications and envision and design better AI/ML applications that are ethical, specifically, those that are inclusive and fair. In our work, we focus on child-centered AI and inclusion. Using a two-fanged approach to inclusion and employing design futuring in our research with schools in India and Finland, children critically considered future technology design for all. In this paper, we present three cases of this work: a study with students at a school in New Delhi and two studies with students at schools in Oulu. Our work showcases how to design for inclusion - by designing for all, and how to design inclusively - by inviting children to envision the future, through design futuring approaches.

Eren Kurshan

Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. We present SAGI, a Systematic Approach to AGI that utilizes system design principles to overcome the energy wall and alignment challenges. This paper asserts that AGI can be realized through multiplicity of design specific pathways and customized through system design rather than a singular overarching architecture. AGI systems may exhibit diver architectural configurations and capabilities, contingent upon their intended use cases. Alignment, a challenge broadly recognized as AIs most formidable, is the one that depends most critically on system design and serves as its primary driving force as a foundational criterion for AGI. Capturing the complexities of human morality for alignment requires architectural support to represent the intricacies of moral decision-making and the pervasive ethical processing at every level, with performance reliability exceeding that of human moral judgment. Hence, requiring a more robust architecture towards safety and alignment goals, without replicating or resembling the human brain. We argue that system design (such as feedback loops, energy and performance optimization) on learning substrates (capable of learning its system architecture) is more fundamental to achieving AGI goals and guarantees, superseding classical symbolic, emergentist and hybrid approaches. Through learning of the system architecture itself, the resulting AGI is not a product of spontaneous emergence but of systematic design and deliberate engineering, with core features, including an integrated moral architecture, deeply embedded within its architecture. The approach aims to guarantee design goals such as alignment, efficiency by self-learning system architecture.

Alessandro Pinto

Several sources of uncertainty have to be taken into account in the analysis and design of CPS. The set of parameters used in the model of the physical plant of a CPS may be uncertain due, for example, to manufacturing processes that are precise up to some bounded tolerance. Physical quantities are sensed by electronic components that add noise to the sensed signals. Abstraction of the physical world, which is often necessary to limit the complexity of the models used in analysis and at run-time in decision-making, leads to non-determinism. The cyber side of a CPS, which includes both hardware and software components, exposes several types of uncertainty such as failures, latency, and implementation errors. Design processes and tools allow engineers to minimize the impact of these types of uncertainty, and to deliver systems which can be operated with an acceptable level of risk. In the past several years, cyber-physical systems have evolved, primarily due to pervasive connectivity, miniaturization, cost-effectiveness of hardware, and advances in the area of Artificial Intelligence. This new class of applications features an environment that is much more complex to model than traditional physical systems due not only to their scale, but also to new sources and types of uncertainty. Consider, for example, the typical case of echo chambers which is attributed to the effect that machine learning algorithms have on the bias of people. Such behavior is not easily predictable because of high uncertainty in the environment (people), which is only approximately represented by machine learning models, but that is inherently due to lack of knowledge. New models and analysis methods are therefore needed to capture different types of uncertainties, and to analyze these new classes of systems.

P. Spagnoletti, A. Resca, Gwanhoo Lee

This research proposes and validates a design theory for digital platforms that support online communities (DPsOC). It addresses ways in which digital platforms can effectively support social interactions in online communities. Drawing upon prior literature on IS design theory, online communities, and platforms, we derive an initial set of propositions for designing effective DPsOC. Our overarching proposition is that three components of digital platform architecture (core, interface, and complements) should collectively support the mix of the three distinct types of social interaction structures of online community (information sharing, collaboration, and collective action). We validate the initial propositions and generate additional insights by conducting an in-depth analysis of an European digital platform for elderly care assistance. We further validate the propositions by analyzing three widely used digital platforms, including Twitter, Wikipedia, and Liquidfeedback, and we derive additional propositions and insights that can guide DPsOC design. We discuss the implications of this research for research and practice.