Hasil untuk "Details in building design and construction. Including walls, roofs"

Fangping Lan, Abdullah Aljebreen, Eduard C. Dragut

URLs serve as bridges between social media platforms and the broader web, linking user-generated content to external information resources. On Twitter (X), approximately one in five tweets contains at least one URL, underscoring their central role in information dissemination. While prior studies have examined the motivations of authors who share URLs, such author-centered intentions are difficult to observe in practice. To enable broader downstream use, this work investigates reader-centered interpretations, i.e., how users perceive the intentions behind hyperlinks included in posts. We develop an intent taxonomy for including hyperlinks in social posts through a hybrid approach that begins with a bottom-up, data-driven process using large-scale crowdsourced annotations, and is then refined using a large language model (LLM) assistance to generate descriptive category names and precise definitions. The final taxonomy comprises 6 top-level categories and 26 fine-grained intention classes, capturing diverse communicative purposes. Applying this taxonomy, we annotate and analyze 1,000 user posts, revealing that advertising, arguing, and sharing are the most prevalent intentions. We further compare our taxonomy with existing taxonomies and demonstrate its utility in a microblog retrieval task by incorporating intent as an additional feature. Overall, our taxonomy provides a foundation for intent-aware information retrieval and NLP applications, enabling more accurate retrieval, recommendation, and interpretation of social media content.

Xiao Liu, Xianlong Yang, Weijun Wang et al.

Faced with complex and unstructured construction environments, wheeled and tracked robots exhibit significant limitations in terrain adaptability and flexibility, making it difficult to meet the requirements of autonomous operation. Inspired by ants in nature, this paper proposes a leg configuration design and optimization method tailored for construction scenarios, aiming to enhance the autonomous mobility of construction robots. This paper analyzes the full operational motion performance of the leg during both swing and stance phases. First, based on kinematic modeling and multi-dimensional workspace analysis, the concept of an "improved workspace" is introduced, and graphical methods are used to optimize the leg dimensions during the swing phase. Furthermore, a new concept of "average manipulability" is introduced based on the velocity Jacobian matrix, and numerical solutions are applied to obtain the leg segment ratio that maximizes manipulability. To overcome the difficulties associated with traditional analytical methods, virtual prototype simulations are conducted in ADAMS to explore the relationship between the robot body's optimal flexibility and leg segment proportions. In summary, the leg segment proportions with the best comprehensive motion performance are obtained. This study presents the first multi-dimensional quantitative evaluation framework for leg motion performance tailored for construction environments, providing a structural design foundation for legged construction robots to achieve autonomous mobility in complex terrains.

Yuxuan Yin, Yu Wang, Boxun Xu et al.

Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine learning based optimization strategy, has been leveraged to automate analog design given its applicability across various circuit topologies and technologies. Traditional BO methods employ black box Gaussian Process surrogate models and optimized labeled data queries to find optimization solutions by trading off between exploration and exploitation. However, the search for the optimal design solution in BO can be expensive from both a computational and data usage point of view, particularly for high dimensional optimization problems. This paper presents ADO-LLM, the first work integrating large language models (LLMs) with Bayesian Optimization for analog design optimization. ADO-LLM leverages the LLM's ability to infuse domain knowledge to rapidly generate viable design points to remedy BO's inefficiency in finding high value design areas specifically under the limited design space coverage of the BO's probabilistic surrogate model. In the meantime, sampling of design points evaluated in the iterative BO process provides quality demonstrations for the LLM to generate high quality design points while leveraging infused broad design knowledge. Furthermore, the diversity brought by BO's exploration enriches the contextual understanding of the LLM and allows it to more broadly search in the design space and prevent repetitive and redundant suggestions. We evaluate the proposed framework on two different types of analog circuits and demonstrate notable improvements in design efficiency and effectiveness.

Xiao Xiang Zhu, Qingyu Li, Yilei Shi et al.

Understanding how buildings are distributed globally is crucial to revealing the human footprint on our home planet. This built environment affects local climate, land surface albedo, resource distribution, and many other key factors that influence well-being and human health. Despite this, quantitative and comprehensive data on the distribution and properties of buildings worldwide is lacking. To this end, by using a big data analytics approach and nearly 800,000 satellite images, we generated the highest resolution and highest accuracy building map ever created: the GlobalBuildingMap (GBM). A joint analysis of building maps and solar potentials indicates that rooftop solar energy can supply the global energy consumption need at a reasonable cost. Specifically, if solar panels were placed on the roofs of all buildings, they could supply 1.1-3.3 times -- depending on the efficiency of the solar device -- the global energy consumption in 2020, which is the year with the highest consumption on record. We also identified a clear geospatial correlation between building areas and key socioeconomic variables, which indicates our global building map can serve as an important input to modeling global socioeconomic needs and drivers.

Paula Carolina Lozano Duarte, Florentia Afentaki, Georgios Zervakis et al.

Flexible Electronics (FE) offer distinct advantages, including mechanical flexibility and low process temperatures, enabling extremely low-cost production. To address the demands of applications such as smart sensors and wearables, flexible devices must be small and operate at low supply voltages. Additionally, target applications often require classifiers to operate directly on analog sensory input, necessitating the use of Analog to Digital Converters (ADCs) to process the sensory data. However, ADCs present serious challenges, particularly in terms of high area and power consumption, especially when considering stringent area and energy budget. In this work, we target common classifiers in this domain such as MLPs and SVMs and present a holistic approach to mitigate the elevated overhead of analog to digital interfacing in FE. First, we propose a novel design for Binary Search ADC that reduces area overhead 2X compared with the state-of-the-art Binary design and up to 5.4X compared with Flash ADC. Next, we present an in-training ADC optimization in which we keep the bare-minimum representations required and simplifying ADCs by removing unnecessary components. Our in-training optimization further reduces on average the area in terms of transistor count of the required ADCs by 5X for less than 1% accuracy loss.

Hadi Keramati, Feridun Hamdullahpur

Generative design has been growing across the design community as a viable method for design space exploration. Thermal design is more complex than mechanical or aerodynamic design because of the additional convection-diffusion equation and its pertinent boundary interaction. We present a generative thermal design using cooperative multi-agent deep reinforcement learning and continuous geometric representation of the fluid and solid domain. The proposed framework consists of a pre-trained neural network surrogate model as an environment to predict heat transfer and pressure drop of the generated geometries. The design space is parameterized by composite Bezier curve to solve multiple fin shape optimization. We show that our multi-agent framework can learn the policy for design strategy using multi-objective reward without the need for shape derivation or differentiable objective function.

Ivan Sysoev, James H. Gray, Susan Fine et al.

English has a convoluted relationship between its pronunciation and spelling, which obscures its phonological structure for early literacy learners. This convoluted relationship has implications for early literacy software, particularly for open-ended, child-driven designs. A tempting way to bypass this issue is to use manipulables (blocks) that are directly tied to phonemes. However, creating phoneme-based blocks leads to two design challenges: (a) how to represent phonemes visually in a child-accessible way and (b) how to account for context-dependent spelling. In the present work, we approached these challenges by developing a set of animated, onomatopoeia-based mnemonic characters, one per phoneme, that can take the shape of different graphemes.We applied the characters to a construction-based literacy app to simplify independent word-building for literacy beginners. We tested the app during a 13-week-long period with 4- to 5-year-olds in kindergarten classrooms. Children showed visible interest in the characters and properly grasped the principles of their functioning. However, the blocks were not sufficient to scaffold independent word building, leading children to rely on other scaffolding mechanisms. To test the characters' efficiency as mnemonics, we evaluated their effect on the speed and accuracy of finding phonemes on a keyboard. The results suggest that there were both children who benefitted from the characters in this task and those who performed better without them. The factors that differentiated these two categories are currently unclear. To help further research on phonetic mnemonics in literacy learning software, we are making the characters available to the research community.

Ilya Yuskevich, A. Hein, Kahina Amokrane-Ferka et al.

The latest advances in the design of vehicles with the adaptive level of automation pose new challenges in the vehicle-driver interaction. Safety requirements underline the need to explore optimal cockpit architectures with regard to driver cognitive and perceptual workload, eyes-off-the-road time and situation awareness. We propose to integrate existing task analysis approaches into system architecture evaluation for the early-stage design optimization. We built the discrete-event simulation tool and applied it within the multi-sensory (sight, sound, touch) cockpit design industrial project.

Kumar Ankit, Lima Agnel Tony, Shuvrangshu Jana et al.

This paper details the algorithms involved and task planner for vehicle collaboration in building a structure. This is the problem defined in challenge 2 of Mohammed Bin Zayed International Robotic Challenge 2020 (MBZIRC). The work addresses various aspects of the challenge for Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicle (UGV). The challenge involves repeated pick and place operations using UAVs and UGV to build two structures of different shape and sizes. The algorithms are implemented using the Robot Operating System (ROS) framework and visualised in Gazebo. The whole developed architecture could readily be implemented in suitable hardware.

Afzal Ahmad, Muhammad Adeel Pasha

Convolutional Neural Networks (CNNs) have gained widespread popularity in the field of computer vision and image processing. Due to huge computational requirements of CNNs, dedicated hardware-based implementations are being explored to improve their performance. Hardware platforms such as Field Programmable Gate Arrays (FPGAs) are widely being used to design parallel architectures for this purpose. In this paper, we analyze Winograd minimal filtering or fast convolution algorithms to reduce the arithmetic complexity of convolutional layers of CNNs. We explore a complex design space to find the sets of parameters that result in improved throughput and power-efficiency. We also design a pipelined and parallel Winograd convolution engine that improves the throughput and power-efficiency while reducing the computational complexity of the overall system. Our proposed designs show up to 4.75$\times$ and 1.44$\times$ improvements in throughput and power-efficiency, respectively, in comparison to the state-of-the-art design while using approximately 2.67$\times$ more multipliers. Furthermore, we obtain savings of up to 53.6\% in logic resources compared with the state-of-the-art implementation.

Virginia Estevez, Lasse Laurson

Domain walls in soft permalloy strips may exhibit various equilibrium micromagnetic structures depending on the width and thickness of the strip, ranging from the well-known transverse and vortex walls in narrow and thin strips to double and triple vortex walls recently reported in wider strips [V. Estévez and L. Laurson, Phys. Rev. B {\bf 91}, 054407 (2015)]. Here we analyze the field driven dynamics of such domain walls in permalloy strips of widths from 240 nm up to 6 $μ$m, using the known equilibrium domain wall structures as initial configurations. Our micromagnetic simulations show that the domain wall dynamics in wide strips is very complex, and depends strongly on the geometry of the system, as well as on the magnitude of the driving field. We discuss in detail the rich variety of the dynamical behaviors found, including dynamic transitions between different domain wall structures, periodic dynamics of a vortex core close to the strip edge, transitions towards simpler domain wall structures of the multi-vortex domain walls controlled by vortex polarity, and the fact that for some combinations of the strip geometry and the driving field the system cannot support a compact domain wall.

Holger Dette, Kirsten Schorning, Maria Konstantinou

We consider the problem of efficient statistical inference for comparing two regression curves estimated from two samples of dependent measurements. Based on a representation of the best pair of linear unbiased estimators in continuous time models as a stochastic integral, an efficient pair of linear unbiased estimators with corresponding optimal designs for finite sample size is constructed. This pair minimises the width of the confidence band for the difference between the estimated curves. We thus extend results readily available in the literature to the case of correlated observations and provide an easily implementable and efficient solution. The advantages of using such pairs of estimators with corresponding optimal designs for the comparison of regression models are illustrated via numerical examples.

Dushyant Kumar

We introduce a generalization of Goddard-Kent-Olive (GKO) coset construction of two dimensional conformal field theories based on a choice of a scaled affine subalgebra $\hat{\mathfrak{h}}^s$ of a given affine Lie algebra $\hat{\mathfrak{h}}$. We study some aspects of the construction through the example of Ising CFT as a generalized GKO coset of $\text{su(2)}_1$ with a scaling factor $s=2$.

Fasheng Sun, Min-Qian Liu, Peter Z. G. Qian

Nested space-filling designs are nested designs with attractive low-dimensional stratification. Such designs are gaining popularity in statistics, applied mathematics and engineering. Their applications include multi-fidelity computer models, stochastic optimization problems, multi-level fitting of nonparametric functions, and linking parameters. We propose methods for constructing several new classes of nested space-filling designs. These methods are based on a new group projection and other algebraic techniques. The constructed designs can accommodate a nested structure with an arbitrary number of layers and are more flexible in run size than the existing families of nested space-filling designs. As a byproduct, the proposed methods can also be used to obtain sliced space-filling designs that are appealing for conducting computer experiments with both qualitative and quantitative factors.

Vladimir Kanovei

A $Σ$-construction of Solovay is partially extended to the case of intermediate sets which are not necessarily subsets of the ground model. As an application, we prove that, for a given name $t$, the set of all sets $t[G]$, $G$ being generic over the ground model, is Borel. This result was first established by Zapletal by a totally different descriptive set theoretic argument.

Franz X. Bronold, Rafael L. Heinisch, Johannes Marbach et al.

Plasma walls accumulate electrons more efficiently than ions leading to wall potentials which are negative with respect to the plasma potential. Theoretically, walls are usually treated as perfect absorber for electrons and ions implying perfect sticking of the particles to the wall and infinitely long desorption times for particles stuck to the wall. For electrons we question the perfect absorber model and calculate, specifically for a planar dielectric wall, the electron sticking coefficient $s_e$ and the electron desorption time $τ_e$. For the uncharged wall we find $s_e\ll 1$ and $τ_e\approx 10^{-4}s$. Thus, in the early stage of the build-up of the wall potential, when the wall is essentially uncharged, the wall is not a perfect absorber for electrons. For the charged wall we find $τ_e^{-1}\approx 0$. Thus, $τ_e$ approaches the perfect absorber value. But $s_e$ is still only of the order of $10^{-1}$. Calculating $s_e$ as a function of the wall potential and combining this expression with the quasi-stationary balance equations for the electron and ion surface densities we find the selfconsistent wall potential, including surface effects, to be 30% of the perfect absorber value.

Olivier Richoux, Ayrault Christophe, Adrien Pelat et al.

This paper presents an experimental, numerical and analytical study of the effect of open roof on acoustic propagation along a 3D urban canyon. The experimental study is led by means of a street scale model. The numerical results are performed with a 2D Finite Difference in Time Domain approach adapted to take into account the acoustic radiation losses due to the street open roof. An analytical model, based on the modal decomposition of the pressure field in a horizontal plane mixed with a 2D image sources model to describe the attenuation along the street, is also proposed. Results are given for several frequencies in the low frequency domain (1000-2500 Hz). The comparison of the three approaches shows a good agreement until f=100 Hz at full scale, the analytical model and the 2D numerical simulation adapted to 3D permit to modelize the acoustic propagation along a street. For higher frequency, experimental results show that the leakeage, due to the street open roof, is not anymore uniformly distributed on all modes of the street. The notion of leaky modes must be introduced to modelize the acoustic propagation in a street canyon.

M. Cvetic, N. D. Lambert

We discuss the low energy effective action for the Bosonic and Fermionic zero-modes of a smooth BPS Randall-Sundrum domain wall, including the induced supergravity on the wall. The result is a pure supergravity in one lower dimension. In particular, and in contrast to non-gravitational domain walls or domain walls in a compact space, the zero-modes representing transverse fluctuations of domain wall have vanishing action.

C. Peters, B. Elliott, S. Yu et al.

A 3.2 MeV injector has been designed and built for the Darht II Project at Los Alamos Lab. The installation of the complete injector system is nearing completion at this time. The requirements for the injector are to produce a 3.2 MeV, 2000 ampere electron pulse with a flattop width of at least 2-microseconds and emittance of less than 0.15 p cm-rad normalized. A large high voltage column has been built and installed. The column is vertically oriented, is 4.4 meters long, 1.2 meters in diameter, and weights 5700 kilograms. A novel method of construction has been employed which utilizes bonded mycalex insulating rings. This paper will describe the design, construction, and testing completed during construction. Mechanical aspects of the design will be emphasized.

Ulrich Hermisson

A method of constructing covariant differential calculi on a quantum homogeneous space is devised. The function algebra X of the quantum homogeneous space is assumed to be a left coideal of a coquasitriangular Hopf algebra H and to contain the coefficients of any matrix over H which is the two-sided inverse of one with entries in X. The method is based on partial derivatives. For the quantum sphere of Podles and the quantizations of symmetric spaces due to Noumi, Dijkhuizen and Sugitani the construction produces the subcalculi of the standard bicovariant calculus on the quantum group.