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This article reflects on the graphic representation of collective housing projects based on the concept of an open plan. This reflection is addressed by exploring different conceptualizations developed by some authors who have theorized about housing flexibility. These authors draw a distinction between two strategies for organizing an open plan: concentrating service spaces in nuclei or structuring them through linear bands. These strategies are illustrated by a selection of projects and works carried out in Europe between the mid 80s and the late 90s. These are well-known projects that have been widely disseminated in the literature and have influenced other projects, both in the professional field and in academic training. The study of the selected cases focuses on the expressive resources used in draw-ing the plans, since this is the most appropriate representation for communicating the uses of space. A comparative analysis of the examples leads to the conclusion that some resources are transversal and can be extrapolated to other cases, and thus allow for the development of a graphic discourse on housing flexibility.

Physical design is major key processes in integrating the circuit into the chip area in VLSI circuit design. A netlist is accepted as input by a floorplanning stage, provided by the physical design phase of splitting the circuits. The floor planner creates the best positions for the circuit module. The dimensions, size, and connectivity details of the modules are contained in the netlist. It is crucial to create an optimum floorplan by creating metaheuristic optimization algorithms to increase the circuit performance by reducing chip size, wire length, and peak temperature. This scheme always has a complexity to allocate the weights and might consequence in adverse bias with a specific intention. Here, the non-slicing floor plan is studied through the sequence pair (SP) method. SP is a method for packing the blocks using a pair of modules termed sequences. Consequently, optimal parameter (area and wire length) tuning is performed by a new hybrid Integrated Bald Eagle and SMO (IBE-SMO) scheme that combined the approaches such as BES and SMO for optimal FP by means of tuning the wire length and area, taking into consideration as multi-objective. This validates the optimization of wire length and area utilizing IBE-SMO to reduce the length, width and height of the floor plan area. The standard MCNC benchmark circuits were used to test the suggested technique, and the findings show that it decreases the area by 0.7 × 104, and wire length by 0.84 × 105, compared to the existing methods. [ABSTRACT FROM AUTHOR]

Space layout design is a critical aspect of architectural design, influencing functionality and aesthetics. The inherent combinatorial nature of layout design poses challenges for traditional planning approaches; thus, it demands the exploration of novel methods. This paper presents a novel framework that leverages the potential of deep reinforcement learning (RL) algorithms to optimize space layouts. RL has demonstrated remarkable success in addressing complex decision-making problems, yet its application in the design process remains relatively unexplored. We argue that RL is particularly well-suited for the design process due to its ability to accommodate offline tasks and seamless integration with existing computer-aided design software, effectively acting as a simulator for design exploration. Framing space layout design as an RL problem and employing RL methods allows for the automated exploration of the expansive design space, thereby enhancing the discovery of innovative solutions. This paper also elucidates the synergy between the design process and the RL problem, which opens new avenues for exploring the potential of RL algorithms in design. We aim to foster experimentation and collaboration within the RL and architecture communities. To facilitate our research, we have developed SpaceLayoutGym , an environment specifically designed for space layout design tasks. SpaceLayoutGym serves as a customizable environment that encapsulates the essential elements of the layout design process within an RL framework. To showcase the effectiveness of SpaceLayoutGym and the capabilities of RL as an artificial space layout designer, we employ the Proximal Policy Optimization (PPO) algorithm to train the RL agent in selected design scenarios with both geometrical constraints and topological objectives. The study further extends to contrast the effectiveness of PPO agents with that of genetic algorithms, and also includes a comparative analysis with existing layouts. Our results demonstrate the potential of RL to optimize space layouts, offering a promising direction for the future of artificial intelligence-aided design. [ABSTRACT FROM AUTHOR]

Simple Summary: Until recently, radiopharmaceuticals have been underutilized for the curative or the palliative treatment of cancer. Advances in a development program with investigational agents that have a radioactive payload as part of the studied drug product necessitate new ways of drug handling and administration as well as novel logistics for a safe and satisfactory patient experience. This review offers a roadmap for the clinical start-up and implementation of an integrated and leveraged programmatic collaboration for an academic theranostic center. Within this roadmap, there are complex issues of logistics, coordination, medical considerations, radioprotection, receipt and waste of radioactive agents, and patient release priorities. Regulatory agency input also adds to the layout dynamics of the theranostic center, but this roadmap does not cover specific requirements as United States state laws differ considerably. Radiation oncologists, radiopharmacists, nuclear medicine physicians, and medical oncologists have seen a renewed clinical interest in radiopharmaceuticals for the curative or the palliative treatment of cancer. To allow for the discovery and the clinical advancement of targeted radiopharmaceuticals, these stakeholders have reformed their trial efforts and remodeled their facilities to accommodate the obligations of a program centered upon radioactive investigational drug products. Now considered informally as drugs and not beam radiotherapy, radiopharmaceuticals can be more easily studied in the traditional clinical trial enterprise ranging from phase 0–I to phase III studies. Resources and physical facilities allocated to radiopharmaceuticals have brought forth new logistics and patient experience for safe and satisfactory drug delivery. The clinical use of theranostic agents—that is, diagnostic and therapeutic radionuclide pairs—has accelerated radiopharmaceutical development. [ABSTRACT FROM AUTHOR]

The creation of a 2D floor plan is an integral part of finishing a building construction. Legal obligations in different states often include submitting a precise floor plan for ownership purposes, as the building needs to be divided between new residents with reasonable precision. Common practice for floor plan generation includes manual measurements (tape or laser) and laser scanning (static or SLAM). In this paper, a novel approach is proposed using spherical photogrammetry, which is becoming increasingly popular due to its versatility, low cost and unexplored possibilities. Workflow is also noticeably faster than other methods, as video acquisition is rapid, on a par with SLAM. The accuracy and reliability of the measurements are then experimentally verified, comparing the results with established methods. [ABSTRACT FROM AUTHOR]

The automatic identification of various design elements in a floor-plan image has gained increasing attention in recent research. Emergency-evacuation applications can benefit greatly from automated floor-plan solutions, as they allow for the development of horizontal solutions instead of vertical solutions targeting a specific audience. In addition to that, current evacuation plans rely on static signs without taking into account the dynamic characteristics of each emergency case. This work aims to extract information from a floor-plan image and transform it into a graph that is used for pathfinding in an emergency evacuation. First, the basic elements of the floor-plan image, i.e., walls, rooms and doors, are identified. This is achieved using Panoptic-DeepLab, which is a state-of-the-art deep neural network for the panoptic segmentation of images, and it is available from DeepLab2, an image segmentation library. The neural network was trained using CubiCasa5K, a large-scale floor-plan image dataset containing 5000 samples, annotated into over 80 floor-plan object categories. Then, using the prediction of each pixel, a graph that shows how rooms and doors are connected is created. An application that presents this information in a user-friendly manner and provides graph editing capabilities was developed. Finally, the exits are set, and the optimal path for evacuation is calculated from each node using Dijkstra’s algorithm.

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We present a novel vectorized indoor modeling approach that converts point clouds into building information models (BIM) with concise and semantically segmented polygonal meshes. Existing methods detect planar shapes and connect them to complete the scene. Some focus on floor plan reconstruction as a simplified problem to better analyze connectivity between planes of floors and walls. However, the connectivity analysis is still challenging and ill-posed with incomplete point clouds as input. We propose ArrangementNet to estimate scene arrangements from an incomplete point cloud, which we can easily convert into a BIM model. ArrangementNet is a novel graph neural network that consumes noisy over-partitioned initial arrangements extracted through non-learning techniques and outputs high-quality scene arrangement. The core of ArrangementNet is an extended graph convolution that leverages co-linear and co-face relationships in the arrangement and improves the quality of prediction in complex scenes. We apply ArrangementNet to improve floor plan and ceiling arrangements and enrich them with semantic objects as scene arrangements for scene generation. Our approach faithfully models challenging scenes obtained from laser scans or multiview stereo and shows significant improvement in BIM model reconstruction compared to the state-of-the-art. Our code is available at https://github.com/zssjh/ArrangementNet. [ABSTRACT FROM AUTHOR]

People tend to take their spatial cognition and wayfinding behaviors for granted while moving about in familiar spaces or traversing regular routes (e.g., the way to work). However, when an emergency occurs, even if people evacuate from a familiar venue, they are still likely to experience unexpected and irreparable tragedy. This study conducted an on-site experiment and a survey investigation. First-person view (FPV) floor plans were adopted to develop a relevant experiment, which was then used to investigate the relationship between wayfinding behavior and two influencing factors: floor plan cognition and distance. The t-tests for the accompanying questionnaire indicated that women (31%) are better than men (5.3%) in legend recognition and men (25.5%) outperform women (7.1%) in orientation; both findings achieved significance and are consistent with the results of previous studies conducted by neuroscientists. One-way ANOVA showed that when participants read a floor plan that was difficult to understand (not FPV), they took considerably more time (153.82 s) to reach the closer staircase than those who read a floor plan that was easy to understand and headed to the farther staircase (113.40 s). The understandability of floor plans is key to affecting the public's evacuation time. [ABSTRACT FROM AUTHOR]

A growing majority of people living in Residential Care Facilities (RCFs) for older people have dementia. Yet the implementation of evidence-based Dementia Design Principles (DDPs), known to reduce symptoms and improve wellbeing, remains limited. This paper reports on the development and application of Plan-EAT, a floorplan-based method of assessing dementia design quality in RCFs. Through the analysis of 34 published architectural exemplars, the method identifies strengths and weaknesses in the layouts of residential care buildings, across 39 assessment criteria organized under 9 DDPs. The paper concludes that the Plan-EAT could benefit architectural practice by providing an evidence-based means of assessing layout planning quality, in both existing cases and emerging RCF design proposals. The findings also highlight the need for care, where reliance is placed on published case studies, to inform the design of dementia supportive environments. [ABSTRACT FROM AUTHOR]

The floor plan is a key part of architectural design and has the characteristics of multi-objective evaluation. Traditional methods are often laborious and may lead to re-work due to optimisation, which affects efficiency. Related research uses various algorithms to generate floor plans to improve efficiency. Based on the leading role of architects on floor plans, this paper proposes a generative design method that combines the design process. It takes the space shape as the starting point, simplifies the design process into a mathematical model, and uses Rhino and Grasshopper to complete the algorithm development. Using different algorithm combinations, a large number of new layouts can be explored, and building layouts can be adjusted freely through data optimisation without having to redesign. The design evolution of floor plans of different cases shows that such a generative design method is feasible, reusable, and more efficient than traditional methods.

Location-based services and applications have grown rapidly over the past decade. These location-based services and applications usually use maps to display user locations. However, the availability of digital indoor maps and indoor route information is far from satisfactory. At present, most indoor applications rely on manually created indoor maps, which requires huge costs and effort for editing and maintenance. Because mobile phones have rich sensors, scientists hope to explore the use of smartphone crowdsourcing to generate indoor maps in recent years. This article provides a systematic review of these works. Unlike former surveys, we divide the indoor maps into indoor route maps and indoor floor plans. For these two kinds of indoor maps, we summarize their common steps and differences in the crowdsourcing-based map learning process. Basically, they share the trajectory collection, landmark detection, trajectory alignment, and graph optimization steps. The floor plan learning needs additional trajectory segmentation and boundary extraction steps. We introduce the state-of-the-art techniques of each of these key processing steps. Finally, we systematically compare existing map learning systems of these two categories in terms of sensor used, users’ participation types, and key technologies used in different steps.

The reorganization of residential areas through mass relocation has had 3 impacts on the living environment of the village. Owing to the reduction in the area of the site, the residential space has lost the room for inviting non‐family members. The number of houses with gardens and fields has also decreased. As the density of housing has increased, more people are concerned about the view from the outside. As a result, many houses close their curtains. The trust among residents is still high today. [ABSTRACT FROM AUTHOR]

A floor plan represents the blue print of a building. Organizing a massive set of such floor plans and accessing them based on similarity is challenging for any architect. During the digitization process printed floor plan images are rotated slightly by a small degree of angle. Handcrafted feature-based methods proposed in the literature fail to generalize on such scenarios efficiently. In this paper we propose a deep learning-based model, Rotation Invariant Siamese Convolution Network (RISC-Net), which is able to retrieve similar floor plan images from the dataset, even in the presence of rotation. Uniqueness of RISC-Net is the ability to handle scan-time rotation both in the query as well as the images in the database. The proposed method is trained and evaluated on a publicly available floor plan image ROBIN dataset and achieved the best retrieval results 79% as compared to the state-of-the-art methods proposed in the same problem domain. [ABSTRACT FROM AUTHOR]

Automatically representing the semantics and topology of indoor building spaces from floor-plans is necessary for many applications, such as architectural design and indoor renovations. Extensive studies have investigated reconstructing indoor spaces with semantics and topology using professional means (e.g., laser scanning and photogrammetry). Floor-plan raster maps are widely and freely available for various purposes. Nevertheless, there is little research on the semantic and topological representation of indoor elements from floor-plan raster maps. To fill this gap, we propose a method of automatically representing the semantics and topology of indoor spaces from floor-plan raster maps. The proposed method first identifies basic geometric primitives from floor-plans using a learning-based hierarchical segmentation approach. Second, the relationship between the detected geometric primitives is assembled into the planar structure representation with topological data using mixed integer programming. Finally, the floor-plan graph structure is checked and optimized to maintain consistency with a polygonal coordinate descent strategy, resulting in a correct representation of the semantics and topology of the indoor space. Comprehensive evaluations demonstrate that the proposed method effectively achieves superior performance in three different datasets. The proposed method allows for 3D model popups for better visualizations and direct architectural model manipulations of the interior building layouts computation.

Abstract The reorganization of residential areas through mass relocation has had 3 impacts on the living environment of the village. Owing to the reduction in the area of the site, the residential space has lost the room for inviting non‐family members. The number of houses with gardens and fields has also decreased. As the density of housing has increased, more people are concerned about the view from the outside. As a result, many houses close their curtains. The trust among residents is still high today.

Purpose: The field study was to understand older adults' reactions to and use of different low-light conditions while walking to bathrooms in the dark in their homes. Low-light conditions included participants' usual nightlights and a destination-based LED strip lighting system. Background: Older adults encounter fall accidents while going to bathrooms at night due to low illuminance levels. They also fear falling due to previous fall histories or visual impairments. This field study tested and compared a destination-based LED strip lighting system with their usual nightlights on their movement and fear of falls. Methods: Fifteen older adults from an independent living facility participated in the within-subject design experiment, walking under two scenarios in random order: with usual nightlights turned on or with the destination-based LED strip lighting system turned on. Body-worn sensors were used to collect participants' movement behaviors, and subjective questionnaires were used to understand participants' anxiety under the two low-light conditions. Further, semi-structured interviews were conducted to understand their nightlight usage patterns and their evaluations of the destination-based LED strip lighting system. Results: Participants walked more smoothly under the destination-based LED strip lighting system scenario. However, the anxiety states were not statistically different between the two scenarios. Conclusion: Visual cues in the dark can benefit older adults' safe movement. However, the application of the lab-effective LED strip lighting system in home settings should consider older adults' floor plans and their furniture layout, both indoor and outdoor ambient lighting sources, and their lifestyles. [ABSTRACT FROM AUTHOR]

SCRUM is a software development life cycle that will work with collaboration to produce products quickly but still provide quality. By using SCRUM, it will increase productivity and increase mutual trust, togetherness, responsibility, ideas communication and creativity of team members. The stakeholder's expectation to build an e-ticketing system fits with the agile nature of SCRUM. Every process in SCRUM must run well starting from data collection, needs analysis, making product backlog, making sprint backlog, daily scrum meeting, sprint review until sprint retrospective must be carried out to achieve success. The applications built in this research will run on web browsers, android and API designs to be integrated with other applications. The research was conducted for two months by working on three product backlogs, then each product backlog would be broken down into three sprints. The results obtained in the study were able to answer the question of the problem that was built with the conclusion that the product owner's ability to communicate with stakeholders and daily scrum meetings was necessary indetermining success in SCRUM

Generating architectural layouts from sites to flats, encompassing site layouts (SLs), building layouts (BLs), and flat layouts (FLs), presents a complex process. Notably, the BL generation is challenging due to the small scale of data, making it difficult to train effective neural networks. This paper introduces an approach for generating layouts throughout the complete process. Initially, it proposes an enhanced generative adversarial network (GAN) combined with the transformer for Stable Diffusion (TranSD-GAN), considering design boundaries and requirements. Subsequently, for generating BLs with small-scale datasets, the paper proposes a stacking transfer learning method. Following this, image operations are conducted to support the flow of building information. Ultimately, BIM models are created at each stage. Through comparative experiments involving neural networks and generation cases, it is demonstrated that the proposed method significantly improves the generative capabilities of small-scale datasets and effectively aids designers throughout the layout design from sites to flats. • Architectural layouts from sites to flats are generated automatically. • An enhanced GAN with the transformer from Stable Diffusion is proposed. • Stacking transfer learning is proposed for a small-scale building layout dataset. • Image operations are performed to streamline building information flow. [ABSTRACT FROM AUTHOR]

Indoor environmental quality plays a crucial role in determining the overall quality of life. This study aims to develop a design optimization approach for the floor plan of public housing buildings with modular flat design in Hong Kong, with focus on enhancing natural ventilation, reducing noise levels, and improve daylighting conditions. The evaluation of these environmental factors was conducted using deep neural network models and a mathematically based Calculation of Road Traffic Noise model. A general floor plan representation was developed for three- and four-winged structures of public housing buildings. An optimization approach utilizing Bayesian optimization was applied to three studied cases: Hung Shing House, Hung Hei House, and Cheung Tai House. The optimization process resulted in an average 41.5 % improvement in average natural ventilation rate. The optimized building shapes effectively served as noise barriers, leading to an average reduction of 20 % in average noise levels. The existing window configurations of each unit type under the modular flat design already provided sufficient daylighting, resulting in only a minor improvement from the optimization process. • An optimization approach of floor plan configuration for public housing was developed. • The optimized floor plan significantly improved the indoor environmental quality. • The design optimization method can be used in early-stage design for public housing. [ABSTRACT FROM AUTHOR]

Indoor scene reconstruction and generating a 2D/3D floor plan is a widely explored problem. In the recent years a few algorithms have been proposed, which either use RGB-D images, requiring a depth capturing camera or depend upon panoramic images, assuming little to no occlusion in the room. In this work, we propose a framework named GRIHA (Generating Room Interior of a House using ARcore), which takes advantage of RGB images taken from a conventional mobile phone camera. The proposed work uses Simultaneous Localization And Mapping (SLAM) technology to estimate the 3D transformations required for layout generation. GRIHA uses SLAM based Google ARcore library for camera pose estimation while capturing the images. It gives the user freedom to generate a layout by merely taking a few conventional photos, rather than relying on specialized depth hardware or occlusion-free panoramic images. We have compared GRIHA with other existing methods and obtained superior results. Moreover, the system is tested on multiple hardware platforms to test the dependency and efficiency. [ABSTRACT FROM AUTHOR]

The automatic generation of building boundaries in contemporary research and engineering projects and practices is dominantly characterized by interior functional constraints. As a basis for the automated generation of various building boundaries, the solution presented in this paper is a novel approach that ignores the internal (functional) and focuses only on the external (non-functional) impacts. The primary orientation on external impacts may be, at any instance, extended by suitable complementary traditional methodology. The applied research methodology and presented method rely on a developed extendible rule-based system that simplifies floor plan creation by the recursive application of a formulated spatial grid generation algorithm. Based on starting parameter values (mainly the lot and building area spaces) the algorithm tends to create a set of grids that satisfy initial constraints by marking the individual grid cells as a part of the building or empty. The presented conceptual framework model served as a foundation for creating a prototype software application that supports the experimental generation of grid arrays that are transformed into readable images of residential building boundaries. For the initial validation of the developed methodology, method, and algorithm, the concrete parametric resolution is set to 1 m. The comparative analysis has shown that the presented approach overcomes some of the limitations of previous related research that generate building boundaries in simple rectangular form or with limited variability. The proposed method, in its current stage, outperforms discussed existing methods concerning complex shape boundary building plan generation. Besides that, there is a broad space for further enhancement directions concerning the interoperability with other, independently developed, frameworks, and software tools. [ABSTRACT FROM AUTHOR]

The presented text considers a floor plan of the medieval church, among other things, from the point of view of semiotics, as developed by C. S. Peirce. Based on his second trichotomy, the text identifies the floor plan as an icon and ascribes to it the properties of a pictogram. The text then deals with an example of using the floor plan of a medieval Christian church in contemporary art, specifically in the work of Bohdan Hostiňák (*1968). The author attempts to analyse Hostiňák‘s 1996 painting Larva, partly precisely from the perspective of Peirce‘s general semiotics. It brings closer the artist‘s work with the opposites of two dominant signs, the temple and the larva, which like elements of harmony and chaos, can evoke feelings of ambivalence in the viewer. He then considers these in terms of Heraclitus‘s philosophy, Nietzsche‘s consideration of the Apollonian and Dionysian, and anticlerical reading of the image, Christian doctrine, entomology, and etymology. [ABSTRACT FROM AUTHOR]

Image captioning is a widely known problem in the area of AI. Caption generation from floor plan images has applications in indoor path planning, real estate, and providing architectural solutions. Several methods have been explored in the literature for generating captions or semi-structured descriptions from floor plan images. Since only the caption is insufficient to capture fine-grained details, researchers also proposed descriptive paragraphs from images. However, these descriptions have a rigid structure and lack flexibility, making it difficult to use them in real-time scenarios. This paper offers two models, description synthesis from image cue (DSIC) and transformer-based description generation (TBDG), for text generation from floor plan images. These two models take advantage of modern deep neural networks for visual feature extraction and text generation. The difference between both models is in the way they take input from the floor plan image. The DSIC model takes only visual features automatically extracted by a deep neural network, while the TBDG model learns textual captions extracted from input floor plan images with paragraphs. The specific keywords generated in TBDG and understanding them with paragraphs make it more robust in a general floor plan image. Experiments were carried out on a large-scale publicly available dataset and compared with state-of-the-art techniques to show the proposed model's superiority. [ABSTRACT FROM AUTHOR]

A large proportion of indoor spatial data is generated by parsing floor plans. However, a mature and automatic solution for generating high-quality building elements (e.g., walls and doors) and space partitions (e.g., rooms) is still lacking. In this study, we present a two-stage approach to indoor mapping and modeling (IMM) from floor plan images. The first stage vectorizes the building elements on the floor plan images and the second stage repairs the topological inconsistencies between the building elements, separates indoor spaces, and generates indoor maps and models. To reduce the shape complexity of indoor boundary elements, i.e., walls and openings, we harness the regularity of the boundary elements and extract them as rectangles in the first stage. Furthermore, to resolve the overlaps and gaps of the vectorized results, we propose an optimization model that adjusts the rectangle vertex coordinates to conform to the topological constraints. Experiments demonstrate that our approach achieves a considerable improvement in room detection without conforming to Manhattan World Assumption. Our approach also outputs instance-separate walls with consistent topology, which enables direct modeling into Industry Foundation Classes (IFC) or City Geography Markup Language (CityGML). [ABSTRACT FROM AUTHOR]

• We have introduced a novel algorithm for outer shape extraction from floor plan images. • Proposed geometric features for rotation invariant floor plan matching. • Performed detailed experiments on original and rotated samples of ROBIN [29] dataset. [Display omitted] An automatic system for analysis and retrieval of building floor plans images is helpful for the architects while designing new projects and providing recommendations to the buyers. For such systems, query by example is preferred over query by keyword, for which user's requirements must be available in digital image form. Floor plans are converted to digital form by scanning and often get rotated slightly by a certain degree of angle during digitization. In this paper, we have proposed a geometric feature-based approach for floor plan image retrieval and our key contribution is to handle different kinds of rotation and scale while retrieving similar floor plan from the database. Our framework is divided into three phases, namely outer shape feature extraction, internal object feature extraction, followed by matching and retrieval. For our experimentation, we have rotated images of ROBIN dataset as currently no rotated floor plan dataset was available. Our experiment shows that the proposed methodology outperforms recent competing methods. [ABSTRACT FROM AUTHOR]