Design Goals Research Articles

AI-assisted design of lightweight and strong 3D-printed wheels for electric vehicles.

The automotive industry is undergoing a transformative shift towards electric vehicles (EVs), driven by environmental concerns and technological advancements. One critical aspect of EV design is the development of lightweight yet robust components, including 3D vehicle wheels. This research explores the implementation of generative models in Computer-Aided Design (CAD) systems to optimize the design of 3D vehicle wheels for electric vehicles. Through the use of generative design and additive manufacturing, we aim to create vehicle wheels that are energy-efficient, aesthetically pleasing, and structurally sound. Electric vehicles are gaining popularity due to their environmental benefits and reduced operating costs, making lightweight and strong wheels an important design goal. This research proposes a novel approach for designing lightweight and strong 3D vehicle wheels for EVs using generative models. The proposed approach involves the following steps: collect and prepare data, choose a generative model architecture, train the generative model, and generate new wheel designs. The approach methods show potential to revolutionize the design and manufacturing of lightweight and strong 3D-printed wheels for electric vehicles. In conclusion, generative models can be used to design and optimize wheel designs, making it possible to create safer, more efficient, and more cost-effective wheels.

BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning

In federated learning (FL), due to the non-i.i.d. nature of distributedly owned local datasets, personalization is an important design goal. In this paper, we investigate FL scenarios in which data owners are related by a network topology (e.g., traffic prediction based on sensor networks). Existing personalized FL approaches cannot take this information into account. To address this limitation, we propose the Bilevel Optimization enhanced Graph-aided Federated Learning (BiG-Fed) approach. The inner weights enable local tasks to evolve towards personalization, and the outer shared weights on the server side target the non-i.i.d problem enabling individual tasks to evolve towards a global constraint space. To the best of our knowledge, BiG-Fed is the first bilevel optimization technique to enable FL approaches to cope with two nested optimization tasks at the FL server and FL clients simultaneously. Theoretical analysis shows that BiG-Fed is guaranteed to converge in an efficient manner. Extensive experiments on both synthetic and real-world data demonstrate significant superior performance of BiG-Fed over seven state-of-the-art methods.

Lessons Learnt from Testing Three Spatial Observation Tools in a Hospital Ward.

Observation tools are increasingly important in healthcare building design research. They enable us to understand how the design of healthcare buildings affects users' health and organisational outcomes. Observations are used in case studies and pre- and post-occupancy evaluations. However, these case studies often struggle to pinpoint the specific design features responsible for observed outcomes. Additionally, harnessing collective knowledge from multiple cases can be challenging. This underscores the need for structured observations. The paper describes the lessons learnt from using three spatial observation tools as part of a study to assess a hospital ward design. Its goal is to reflect on the purpose, usability, advantages, disadvantages, and future improvements of these tools and to offer insights into their potential to support research on hospital ward design. The first tool, by the Centre for Health Design, utilizes a checklist-style matrix to evaluate the design of patient rooms, assessing 17 Evidence Based Design goals, including patient safety, worker safety and effectiveness, quality of care, patient experience, and organizational performance. The second tool is a one-time observation tool, a structured spatial inventory aimed at documenting design features throughout the entire hospital ward, covering elements such as room size, access to daylight, natural elements, furniture, safety measures, and more. It can be combined with the third tool; the recurring observation tool, which focuses on monitoring usage, users, their activities, and behaviour across various types of environments, including patient, staff, care, and supportive spaces. The last two observation tools were developed for the research project, adapting the Smart Sustainable Offices method for healthcare environments. This paper emphasizes the importance of selecting suitable observation tools for specific research objectives, providing guidance for working with observations and conducting pre-and post-studies. While not aimed at validating observation tools, it offers reflections to aid in development and use of observation tools. Finally, documenting spatial contexts enhances understanding of study findings, and reusing observation tools enables cross-study comparisons, with future potential for leveraging artificial intelligence.

Using Collective Intelligence to Develop Design Requirements for a Complex Intervention for Advance Care Planning in the Community.

Engaging people in advance care planning is a challenging systemic problem that requires a social innovation approach and a conceptual framework to guide behavioural and social change efforts. To identify stakeholders' perspectives on barriers to advance care planning engagement, options for overcoming these barriers, and user needs. The findings will inform the design of a health behaviour change intervention for engaging older adults (50+) in advance care planning. To advance co-production and intervention design goals, the study used collective intelligence and scenario-based design methods. Following a systematic stakeholder analysis, 22 participants were recruited to three online collective intelligence sessions. The socioecological perspective informed framing of integrated findings and specifying factors at the individual, interpersonal, service, and system levels. Identified barriers (n = 109) were grouped into seven categories: (i) Psychological, (ii) Advance Care Planning Literacy, (iii) Interpersonal and Interprofessional, (iv) Service-Related, (v) Resources and Supports, (vi) Advance Care Planning Process and Methods, (vii) Cultural and Societal. Stakeholders generated 222 options for overcoming these barriers and specified 230 service user needs. The need to change perceptions of advance care planning, increase psychological readiness, and target advance care planning literacy was highlighted (individual-level). Timely, focused, and meaningful interaction between the key ACP actors must be facilitated using creative strategies (interpersonal-level). Need- and value-based services, including high quality resources, support systems, and infrastructure, should be co-designed (service-level). Cultural and societal transformation is required (system-level). Findings integration offered insight into the complexity of the design context and problem situation and identified directions for context-specific advance care planning intervention development. The use of design thinking methodologies is recommended for the next phase of complex intervention development. The study presents a roadmap of actions required from policy-makers, practitioners, and researchers to ensure the design of adequate advance care planning interventions. Quality of reporting was assured by adherence to Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (International Journal for Quality in Health Care, 19, 2007, 349). Patient and public representatives participated in the collective intelligence sessions. Members of the All Ireland Institute of Hospice and Palliative Care Voices4Care facilitated that process. Findings from the first CI session (involving patients and caregivers) informed the content, format, and methods used in subsequent CI sessions.

Advanced design and analysis of dual element MIMO antenna for Ultra-Wideband Applications

This investigation uses the Genetic Optimization Method to convert a wide-band MIMO antenna into a UWB (3.1GHz–10.6 GHz) MIMO antenna. Initially, a 22 × 42, mm2 wideband 2X2 MIMO antenna was designed using commercial Flame Retardant-4 material. The structure of the antenna patch was designed using half-circular forms on the half-ground plane. The designed MIMO antenna operates throughout a large frequency range of 4.8–9.8 GHz. While it was successful in achieving broad band characteristics, this MIMO antenna was unable to reach ultra-wide band characteristics. The MIMO antenna was operated in UWB ranges by a parametric study, which indicated that the resonating characteristics can be significantly modified by inserting a rectangular slot into the ground plane. Throughout the operational range, it was important to be concerned about improved gain and good isolation. Accordingly, optimization was carried out in order to accomplish the essential, multi-objective goals. The difficult multi-objective design goal is now reduced to an optimization challenge by means of genetic algorithm based random search. One of the performance goals that this optimization strategy aimed to fulfil was an increase in isolation to –20 dB across the entire Ultra-Wideband, with S11 being below –10 dB from 3.1 to 10.6 GHz. It is recommended to randomly select the parameters from their respective ranges for this work. This led to the selection of an optimizer based on random searches. In the operating range, the UWB has a respectable gain of 4 dB to 6 dB and increased isolation to –20 dB, according to the genetic algorithm optimizer’s execution of the predefined multiple-objective task.

SNoMaN: a visual analytic tool for spatial social network mapping and analysis

ABSTRACT Spatial social networks (SSNs) are node-link structures that evidence interpersonal or inter-organizational relationships, where nodes and edges have a defined geographic location. To model SSNs, users need both geographic and social network metrics. However, there are few GUI-based analytic tools that enable simultaneous spatial and social network exploration. In this paper, following the research framework of Exploratory Spatial Data Analysis (ESDA) and design principles of social network analysis tools, we derived three design goals of exploratory spatial social network analysis (SSNA). Guided by these design goals, we provide a visual analytic tool, SNoMaN, which links network and geographical layouts and helps users conduct SSNA by interactively computing and visualizing SSN metrics, describing spatial distributions, exploring associations, and detecting anomalies. We introduce new types of visual diagrams, including Cluster–Cluster Plots, Centralization Plots, on-the-fly mapping of geometrically bounded network modules, and Route Factor Diagrams. We illustrate these new approaches using use case studies of a 1960s network of Mafia members, a global flight network, and a food donation-sharing network in southwestern Virginia. We find that SNoMaN can be used to generate data insights that fuse a system’s spatial and social dimensions that are hard to obtain otherwise.

Seismic performance of self-tapping bolts-octagonal core sleeve flange square steel column connection

This study proposes a fully bolted connection joint for a square steel column incorporating an octagonal core sleeve. To ensure smooth assembly of the joint at the construction site, a certain gap is maintained between the core sleeve and the square steel column. However, this gap may exert a certain influence on the seismic performance of the joint. To enhance the seismic performance of the joints, the application of self-tapping bolt technology is recommended for optimizing the design of the joints. Based on these considerations, this study designed and conducted quasi-static tests on two specimens: the self-tapping bolt-octagonal core sleeve flange square steel column connection joint (SOFC) and the traditional column-column welded connection joint (TWC). The seismic performance of the two connection joints was compared and analyzed. The test results demonstrate that the SOFC specimen achieves the seismic design goal of “strong-joints and weak-members,” with a maximum bearing capacity of −1128.17 kN·m, which represents a 11.42 % improvement over that of the TWC specimen. Furthermore, the total energy dissipation of the two specimens across all loading levels differs by 11.2 %, indicating that their seismic performance is comparable. Additionally, a finite element numerical simulation was also performed to analyze the failure mode of the SOFC model in the limit state, clarify its failure path, and verify that the use of self-tapping bolts can effectively optimize the joint force transmission and improve the seismic performance.

Lightweight Research on Wireless Charging System for Unmanned Aerial Vehicles Based on High Efficiency

Abstract: Unmanned aerial vehicles (UAVs) are characterized by safety, flexibility, and economy, and are widely used in many fields. At present, due to the limitation of UAV loads and the bottleneck of battery technology, UAVs can't carry out missions for a long time. The wireless charging system for UAVs has the advantages of safety, flexibility, automation, and environmental adaptability, so it has gained wide attention. Electromagnetic induction wireless energy transmission system is a system that transmits electrical energy through a magnetic field, and in electromagnetic induction wireless energy transmission system, it is necessary to reach the goals of lightweight, high efficiency, anti-skewing, and modularization. In order to achieve the goals of high efficiency and anti-deviation, this paper adopts three sets of series-connected rounded rectangular coils as the transmitting stage to improve the charging efficiency and anti-deviation ability; in order to achieve the goal of lightweight, this paper reduces the weight by changing the material of the magnetic core, and using the annulus core amorphous alloy core; in order to achieve the goal of modularity, this paper designs to carry multiple sets of receiving coils, which can be switched on and off according to the demand in order to satisfy the different power requirements. Finally, a bilateral LCC compensation topology network is established to control the input and output currents to achieve the design goal. The method proposed in this paper helps to realize the automatic charging of UAV and improve the endurance of UAV, so as to enhance the working hours and inspection range of UAV, and make UAV capable of doing more work.

Deep neural network-assisted fast and precise simulations of electrolyte flows in redox flow batteries

Flow fields are a key component in redox flow batteries, which is to distribute electrolytes onto electrodes at the maximum uniformity with the minimum pump work. Achieving this design goal requires accurate simulations of electrolyte flows and identification of the dead zones where the flows become weak or stagnant. However, conventional case-by-case numerical simulation requires significant computational resources. In this work, we use deep learning to predict the electrolyte flow in flow batteries with a neural network knows as U-Net. The U-Net is well trained by learning the mapping between the input (flow field geometry) and output (velocity magnitude distribution). Results show that the pixel-wise comparison of the velocity magnitudes between the U-Net-predicted results and finite element simulated results exhibits an average Euclidean distance of 442.6 and an average R2 of 0.979, indicating that the electrolyte distribution can be accurately simulated based on the geometric characteristics of flow fields. In addition, dead zones are precisely identified by labeling the regions with low velocity magnitudes. Modifying the channel depth in these regions substantially enhances the under-rib convection, thereby improving the system efficiency by 5.5 % at 200 mA cm−2. Furthermore, compared to the numerical simulation, the U-Net-assisted prediction significantly reduces the computational time by 99.9 %. It is anticipated that the U-Net-assisted simulation provides an accurate and efficient tool for obtaining the velocity distribution of flow fields that can assist the flow field design especially in large quantities and large scale.

A novel pattern language method for tradespace exploration: application to space mission architecting

With the growing complexity of systems across diverse domains, key challenges emerge in simultaneously designing networks and their underlying infrastructure, requiring resolution at concept stage. In space mission design, technological advancements and ambitious goals enable numerous mission architecture concepts due to diverse material flow options. Current design support tools function either after the initial concept design, or tend to rapidly converge toward point solutions, limiting the solution space. We introduce a digital tool for early-stage concept design, capable of generating, evaluating, and visualising a wide range of user-defined architectural options. It utilises a pattern language for space missions, incorporating building blocks like ‘launch’ or ‘electrolysis’. Validation with historic missions showcase the tool automatically identifies all four main mission architectures considered during the Apollo design and more, and accurately recommending the Lunar Orbit Rendezvous architecture which was actually selected. Predicted masses, informing costs, vary by 15% and duration by 30% from actual values, which is within expectations. Identifying missions and leveraging expert knowledge is also demonstrated in the Mars Sample Return case. This approach can help minimise the risk of overlooking effective mission concepts early on. Furthermore, the method is applicable in other domains facing concurrent infrastructure and logistics design challenges.

Numerical Methods for Topological Optimization of Wooden Structural Elements

Timber represents a building material that aligns with the environmental demands on the impact of the construction sector on climate change. The most common engineering solution for modern timber buildings with large spans is glued laminate timber (glulam). This project proposes a tool for a topological optimized geometry generator of structural elements made of glulam that can be used for building a database of topologically optimized glulam beams. In turn, this can be further used to train machine learning models that can embed the topologically optimized geometry and structural behavior information. Topological optimization tasks usually require a large number of iterations in order to reach the design goals. Therefore, embedding this information into machine learning models for structural elements belonging to the same topological groups will result in a faster design process since certain aspects regarding structural behavior such as strength and stiffness can be quickly estimated using Artificial Intelligence techniques. Topologically optimized geometry propositions could be obtained by employing generative machine learning model techniques which can propose geometries that are closer to the topologically optimized results using FEM and as such present a starting point for the design analysis in a reduced amount of time.

Privacy-enhanced distributed revocable identity management scheme based self-sovereign identity

In recent years, the rapid proliferation of digital services and resources on Industrial Internet has imposed higher demands on universality and privacy of identity management. Particularly with the advent of the digital economy, prudent users are urged to maintain control over their digital identity credentials. However, traditional identity management methods have failed to meet this requirement and thus have been prone to raise users' concerns about potential financial loss. Specifically, conventional identity management systems(IDMS) have been plagued by imperceptible privacy disclosure, which derives from the flaws in single points of failure, excessive disclosure, correlation analysis, traceability, and revocation. The emerging Self-Sovereign Identity (SSI) architecture aims to tackle these issues and is propelling the evolution of privacy-enhanced distributed identity management. To this end, we proposed a privacy-enhanced distributed identity management scheme with sequential aggregate issuance, threshold traceability and revocability in the setting of multiple credential issuers and regulators. We adopted the Decentralized identifiers(DIDs) and verifiable credentials(VCs) based on the SSI architecture to ensure the hierarchical identity authentication. The security and performance analysis shows that our proposal achieves the desired design goals and is feasible for distributed Industrial Internet.

Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process

AI is anticipated to enhance human decision-making in high-stakes domains like aviation, but adoption is often hindered by challenges such as inappropriate reliance and poor alignment with users' decision-making. Recent research suggests that a core underlying issue is the recommendation-centric design of many AI systems, i.e., they give end-to-end recommendations and ignore the rest of the decision-making process. Alternative support paradigms are rare, and it remains unclear how the few that do exist compare to recommendation-centric support. In this work, we aimed to empirically compare recommendation-centric support to an alternative paradigm, continuous support, in the context of diversions in aviation. We conducted a mixed-methods study with 32 professional pilots in a realistic setting. To ensure the quality of our study scenarios, we conducted a focus group with four additional pilots prior to the study. We found that continuous support can support pilots' decision-making in a forward direction, allowing them to think more beyond the limits of the system and make faster decisions when combined with recommendations, though the forward support can be disrupted. Participants' statements further suggest a shift in design goal away from providing recommendations, to supporting quick information gathering. Our results show ways to design more helpful and effective AI decision support that goes beyond end-to-end recommendations.

PressProtect: Helping Journalists Navigate Social Media in the Face of Online Harassment

Social media has become a critical tool for journalists to disseminate their work, engage with their audience, and connect with sources. Unfortunately, journalists also regularly endure significant online harassment on social media platforms, ranging from personal attacks to doxxing to threats of physical harm. In this paper, we seek to understand how to make social media usable for journalists who face constant digital harassment. To begin, we conduct a set of need-finding interviews with Asian American and Pacific Islander journalists to understand where existing platform tools and newsroom resources fall short in adequately protecting journalists, especially those of marginalized identities. We map journalists' unmet needs to concrete design goals, which we use to build PressProtect, an interface that provides journalists greater agency when engaging with readers on Twitter/X. Through user testing with eight journalists, we evaluate PressProtect and find that participants felt it effectively protected them against harassment and could also generalize to serve other visible and vulnerable groups. We conclude with a discussion of our findings and recommendations for social platforms hoping to build defensive defaults for journalists facing online harassment.

Topology optimization of link mechanisms for comprehensive synthesis of component arrangement and structure using micropolar elasticity model

This paper proposes a method for topology optimization of link mechanisms with multiple outputs, using a multi-material micropolar elasticity model. This approach allows for comprehensive optimization of both the arrangement and structure of the link mechanism components. By utilizing a continuum model that incorporates micropolar elasticity, we can specify bending stiffness independently from tensile stiffness, resulting in deformation characteristics that approximate a link mechanism. The optimization problem of designing link mechanisms for multiple outputs is reformulated as a boundary value problem within this model framework. The design goal is to synthesize a link mechanism that not only follows a desired path but also possesses the required degrees of freedom. To achieve this, the objective function is defined by the displacement error under external force and the strain energy in the links. The multi-material micropolar elasticity model is then optimized through a gradient-based optimization method, focusing on this objective function. The effectiveness and applicability of our methodology are demonstrated through several numerical case studies.

Universal NIR-II fluorescence image enhancement via square and square root network

The second near-infrared (NIR-II) fluorescence imaging has attracted widespread attention from researchers due to its powerful advantage of assisting doctors in real-time tumor removal during surgery. Light has different penetration capabilities in different bands. Although NIR-IIb window (1,500-1,700 nm) imaging is clearer than NIR-IIa window (1,000-1,300 nm), there are no effective molecular probes for NIR-IIb clinical transformation. NIR-II images are rich in variety, including animals, tissues, etc., and the huge differences in structure also bring difficulties to image enhancement. In order to solve the above problems, we propose a universal NIR-II fluorescence image enhancement model based on arbitrary style transfer, and simultaneously achieve the quality enhancement of multiple types of NIR-IIa fluorescence images. The goal of this model design is to completely preserve the important information of the original image to prevent artifacts and meet the high demand for real-time imaging during NIR-II surgery. To this end, we design a fusion network based on the calculation method of square-square root-sign assignment, in which the content information of NIR-IIa is integrated into the style representation of NIR-IIb to increase the maintenance of the original content structure, so that the stylized feature map tends to show the content. In addition, we propose an attention matching loss, which extracts key structural information through the attention mechanism and maintains the structure of the enhanced image consistent with the original image. Extensive experiments demonstrate that our proposed model has a great advantage in maintaining the original content information, with the SSIM of 0.625 and the content loss of 0.279 compared to the original images. In addition, the quality of enhanced images is satisfactory, with the PSNR of 31.20 and the SSIM of 0.501 compared to the real images.

Hydraulic characteristics of effective sea lamprey barriers

A network of 494 lowermost barriers on tributaries of the Laurentian Great Lakes prevents invasive sea lamprey (Petromyzon marinus) from accessing upstream spawning habitat and is critical to the success of the sea lamprey control program. The design goal of purpose-built barriers for sea lamprey control at low-head dams was to maintain a minimum vertical separation of 45 cm between the crest and downstream water level and a 15-cm overhanging lip. Due to physical site constraints limiting barrier design height and fluctuating stream water levels, many barriers cannot meet the design criteria. However, some barriers continue to block the passage of the sea lamprey even when the design criteria are not fulfilled. We conducted a physical modeling study of three sea lamprey barriers of varying historical efficacies to understand the hydraulic characteristics of effective barriers better. Results showed that time-averaged and horizontally averaged streamwise velocity, energy dissipation rate, and eddy length scale in the vertical direction strongly correlate with barrier efficacy. We combined the resulting variables into a dimensionless Barrier number that can be used to categorize barrier efficacy. Ineffective barriers generally had higher Barrier numbers than effective barriers. Our experimental investigation suggests that the fluid flow and turbulence conditions near the riverbed are not as crucial to barrier efficacy as those above the riverbed at 50 % of the crest height. Our work improves understanding of how existing barriers block sea lamprey movement, which could aid in the design of future sea lamprey barriers.

A Study on a College Writing Course Model Utilizing Public Service Advertisements

This study proposes a column-writing course model that utilizes public service advertisements (PSAs) and aims to elucidate the educational effectiveness and significance of the course through a survey conducted among learners. The study participants consisted of 99 learners enrolled in the course “Media Society and Critical Writing,” which is part of the mandatory liberal arts curriculum, <Critical Thinking and Expression>, at Soongsil University. The course model is designed with the goal of analyzing various social issues presented in PSAs, collaboratively exploring solutions with peers, and ultimately completing a column. The design reflects Bransford et al. (1998) “IDEAL Problem Solving” method, the Engaged Learning<sup>+</sup> approach implemented at Soongsil University, and the core competencies outlined by the World Economic Forum (2023). This paper aims to enhance learners' creative and analytical thinking, facilitate the establishment of social and self-awareness among students through their communication with others, and assist in articulating these insights in a column format. Additionally, a key design goal of this course model is to enable learners to concretely articulate the process of understanding and empathizing with the difficulties of others, which they may not have personally experienced, through the analysis of PSAs. The survey conducted among the learners revealed that this course model significantly contributed to improving their abilities in media text analysis, communication, and writing.

Quantitative Design of Cathode Materials for Ion Battery from a Reductionist Perspective

AbstractThe quantitative design of functionalities for functional materials is highly attractive for materials research, which must be based on a thorough understanding of the behavior of fundamental particles. Reductionism advocates for the understanding of complex materials through dissection into the constituent parts, providing a robust framework for investigating functional materials. In an ion battery system, this review utilizes reductionism to deconstruct cathode materials into phase, atom, and even electron, building the intrinsic connections between the macroscopic properties and fundamental particles across four degrees of freedom. This aims to enable the quantitative design of cathode materials. Specifically, the microscopic origins of the macroscopic properties, that is, capacity, potential, rate, and cycling reversibility, based on lattice, charge, orbital, and spin degrees of freedom are elucidated. Additionally, current strategies are summarized and proposed future development directions for improving these properties. These insights contribute to achieving the goal of quantitative design of energy storage materials.

Surface Mechanical Property Prediction and Process Optimization of 18CrNiMo7-6 Carburized Steel Stator Guide Based on Radial Basis Function Neural Network and NSGA-II Algorithm

The carburizing process is a key technology that affects the mechanical properties of the surface of the hydraulic motor stator guide rail, and the related process parameters have an important influence on surface hardness, the thickness of the carburized layer, and the deformation of the guide rail. However, at present, the relationship between the carburizing process parameters and the surface mechanical properties of the target is not clear. This paper proposes a “hardness prediction and process parameter optimization” method. Firstly, a finite element model is established, with carburizing time, temperature, and carbon potential as the three input factors; the optimal Latin hypercubic experimental design and sensitivity analysis are applied. Secondly, surface hardness, carburized layer thickness, and deformation are taken as the output values, and an RBF neural network is used to construct the prediction model. The results show that the RBF neural network can be accurately used for the prediction of surface hardness, the thickness of the carburized layer, and deformation, and for the optimization of process parameters. The optimized parameters of surface hardness and the thickness of the carburized layer were increased by 4.2% and 5.1%, respectively, and the deformation amount was reduced to 0.31 mm, achieving the goal of optimal design.