bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evolution of Brain Network Connectivity in the Prefrontal Cortex During Concept Generation Using Brainstorming for a Design Task
Authors: Milovanovic Julie , Hu Mo , Shealy Tripp , Gero John 
Published: November 2020

Abstract
The research results presented in this paper explore the temporal changes in central regions of the prefrontal cortex (PFC) during design brainstorming. Design mobilizes a range of cognitive processes such as problem analysis and framing, concept generation, decision-making, visual reasoning and creative problem solving. Concept generation is supported by an iteration of divergent and convergent thinking. The process of brainstorming focuses primarily on divergent thinking. Measurement techniques from neuroscience were used to quantify neurocognitive activation during concept generation using brainstorming during a design task. Correlations in brain activation were used with graph theory to describe brain network connectivity and present the temporal evolution of network centrality in the PFC during brainstorming. The results reveal shifts of network centrality between the right, medial, and left PFC, suggesting possible shifts in the dominant cognitive functions between divergent and convergent thinking during design brainstorming. The alternations of centrality and connectivity between hemispheres provides a consistent mapping with the theory of dual reasoning process in prior design cognition studies. This empirical study with ten graduate engineering students offers initial results to further explore connections between brain network connectivity and cognitive processes when brainstorming during a design task. It provides new evidence to examine existing theories of design.
Topic Modeling and Sentiment Analysis of Social Media Data to Drive Experiential Redesign
Authors: Song Binyang , Meinzer Emmett , Agrawal Akash , McComb Christopher 
Published: November 2020

Abstract
The elicitation of customer pain points is a crucial early step in the design or redesign of successful products and services. Online, user-generated data contains rich, real-time information about customer experience, requirements, and preferences. However, it is a nontrivial task to retrieve useful information from these sources because of the sheer amount of data, often unstructured. In this work, we build on previous efforts that used natural language processing techniques to extract meaning from online data and facilitate experiential redesign and extend them by integrating a sentiment analysis. As a use case, we explore the airline industry. A considerable portion of potential passengers opt out of traveling by airplane due to aviophobia, a fear of flying. This causes a market loss to the industry and inconvenience for those who experience aviophobia. The potential contributors to aviophobia are complex and diverse, involving physical, psychological and emotional reactions to the air travel experience. A methodology that is capable of accommodating the complexity and diversity of the commercial airline industry user-generated data is necessary to effectively mine customer pain points. To address the demand, we propose a novel methodology in this study. Using passenger commentary data posted on Reddit, the method implements topic modeling to extract common themes from the commentaries and employs sentiment analysis to elicit and interpret the salient information contained in the extracted themes. This paper ends by providing specific recommendations that are germane to the use case as well as suggesting future research directions.
A Dual Quaternion Based Method for Estimating Margins for Planning Target Volumes in Radiotherapy
Authors: Ge Q. J. , Yu Zihan , Langer Mark 
Published: November 2020

Abstract
This objective of this paper is to develop a dual quaternion based method for estimating target volumes in radiation therapy for head and neck cancer. Inaccuracies in radiation targeting are responsible for incidental exposure of healthy adjacent tissues, causing significant morbidity and mortality. This paper focuses on inaccuracies incurred when a tumor is displaced during treatment. To address this problem, the clinical target must be expanded to cover the region through which the tumor might move. The resulting expanded target is known as the Planning Target Volume (PTV). In the current practice, the rotational components of displacements are neglected, producing planning target volumes that either miss the true target motion or are larger than needed to cover the target path. By using the dual quaternion based kinematic formulation, this paper represents and captures both translational and rotational inaccuracies. It then presents a framework for calculating the PTV swept out by the target as it shifts within its range of translations and rotations.
Towards an Understanding of Semantic Memory During Idea Generation
Authors: Mertens Attakias T. , McComb Christopher , Toh Christine A. 
Published: November 2020

Abstract
Research in new product design still lacks an understanding of how the types of information used by designers can lead to more successful designs and what cognitive components are involved in the process of generating new ideas. Some theories have arisen that focus on memory usage that could have an impact in idea generation early on in the design process. This framework forms the basis of the current study, focused on identifying the underlying cognitive processes that are active during the design process. To accomplish this, undergraduate students were recruited from the University of Nebraska-Omaha. During the study, participants were presented a design problem, given information pieces that corresponded to the Information Archetypes Framework, and asked to generate ideas for a solution. Students were then asked to recall the information pieces from memory. Participants’ data were analyzed using Latent Semantic Analysis in order to assess the similarities between generated ideas, recall, and information pieces. Results from this were assessed for relationships using Spearman correlations and simple regression. This study was able to demonstrate memory usage within the early design process.
Life Cycle GHG Assessment of Mixed Construction and Demolition Waste Treatment for End of Life Recovery Facility Design: A Sydney, Australia Case Study
Authors: Moore Tahli , Zhang Hao 
Published: November 2020

Abstract
Historically in Australia, mixed Construction and Demolition waste, and Commercial and Industrial waste has been traditionally landfilled. As environmental impacts of landfilling is becoming more evident New South Wales policy makers and innovators have begun exploring an incineration strategy to use such waste to generate electricity. The objective of this study is to utilise life cycle assessment to evaluate GHG emissions from this waste treatment strategy and the environmental impact of a case study facility, in Sydney Australia. The system boundary includes the thermal treatment of waste through incineration, the electricity generation from the steam turbine and air pollution control processes involved within. The functional unit is based on 1 tonne of input mixed Construction and Demolition waste and Commercial and Industrial waste. GHG emissions are calculated and the result shows that the facility generates 0.994 MWh/tonne waste and 1.16 tCO2e/MWh electricity. This emission is lower than a brown coal fired powerplant emission factor 1.31 tCO2e/MWh. The results from this study assists understanding and policy making for the future of Energy-from-Waste as part of the generation mix in New South Wales, Australia.
Effects of Correlated Noise on the Performance of Persistence Based Dynamic State Detection Methods
Authors: Tempelman Joshua R. , Myers Audun , Scruggs Jeffrey T. , Khasawneh Firas A. 
Published: November 2020

Abstract
The ability to characterize the state of dynamic systems has been a pertinent task in the time series analysis community. Traditional measures such as Lyapunov exponents are often times difficult to recover from noisy data, especially if the dimensionality of the system is not known. More recent binary and network based testing methods have delivered promising results for unknown deterministic systems, however noise injected into a periodic signal leads to false positives. Recently, we showed the advantage of using persistent homology as a tool for achieving dynamic state detection for systems with no known model and showed its robustness to white Gaussian noise. In this work, we explore the robustness of the persistence based methods to the influence of colored noise and show that colored noise processes of the form 1/ f α lead to false positive diagnostic at lower signal to noise ratios for α &lt; 0.
Dimensions of Similarity Used to Identify Products As Sources of Analogy
Authors: Tsoka Arnold N. , Tribaldos Jicmat Ali , Sen Chiradeep 
Published: November 2020

Abstract
This paper presents a human-subject experiment exploring the dimensions of product similarity that designers use to detect a product as a source of analogy during designing another. In the study, fifty voluntary participants are presented with a target product that is to be designed, and five other source products that are similar to the design task in various dimensions such as function, structure, or working principle. The designers are then asked to identify the products that they consider to be useful sources of analogy for designing the target, and to write in plain English why they considered so. These comments are analyzed using a protocol to reveal the dimensions of similarity between the target and the source product that inspired them to select the source. The data comprises of 2,440 total dimension instances among the participants. The results show that the identification of products as sources of analogy is driven by at least six dimensions: working principle, structure, human interaction, function, energy flows, and material flows. Among these, working principle, structure, and human interaction are more dominant than function, which was previously believed to be the sole driver of analogy.
Creating a Design for Inspectability Framework: Investigating DfAM Heuristics for Inspection Technologies
Authors: Mahan Tobias , Stover Micaela , Arguelles Andrea , Menold Jessica 
Published: November 2020

Abstract
Design heuristics created for additive manufacturing (AM) help designers increase manufacturability, reduce waste, and create novel geometries that are generally impossible to create with traditional manufacturing techniques. However, this new manufacturing method has come with its own challenges, specifically for inspection and quality assurance end use parts. Current methods for quality assurance and control are not capable of assessing quality of complex geometries or may be prohibitively expensive for AM produced components. By considering inspection early in the design process, designers can reduce the cost of quality control and prevent expensive redesign associated with non-inspectable components. The Design for Inspectability (DfI) worksheet proposed in the current work serves as a first step towards integrating quality control and quality assurance considerations into early stage design, specifically in the context of AM. The DfI worksheet can be used to assess if a part can be inspected for quality after production and can inform future iterations of design concepts. To determine if the worksheet assists designers in creating a component that can be assessed for quality, a controlled laboratory study was conducted. Findings suggest the worksheet effects design outcomes.
Development of a Robotic Landing System for UAVs Applied in Various Terrains
Authors: Peng Chengyang , Chowdhury A. M. Masum Bulbul , Cheng Jinsai , George Richard L. , Shen Tao 
Published: November 2020

Abstract
Unmanned Aerial Vehicles (UAVs), often referred as drones, have been widely implemented for civilian, commercial, search and rescue, and military operations with the advantages of easy deployment, low cost, automation, as well as, most importantly, allowing the execution of dangerous or difficult tasks remotely and safely. However, current UAVs are equipped with a skid or wheel landing gear that limits the application of UAVs to an even and flat ground for safe landing and taking off; this constraint impedes the development of UAVs for application in extreme environments, such as war fields and remote wilderness where proximate level ground is inaccessible. The ability of UAVs to land on un-level ground would help broaden the application of UAVs; in particular, the ability to go beyond thermal imaging to locate a lost hiker with the ability to land and deliver life-sustaining resources in a more timely manner offers a benefit to human rescue missions. This paper presents an innovative robotic landing system consisting of three slanted legs, each individually controlled by a motor. The footpad of each leg has an integrated force sensor for detecting ground touch. An inclinometer is installed on the platform of the landing system to sense the UAVs orientation during landing. Thus, the landing system can keep the platform horizontal when it lands on the ground by extending or retracting the legs. The feasibility and effectiveness of the robotic method have been demonstrated by several indoor and outdoor experiments.
Bayesian Optimization of Target Buckling Shapes in Constrained Elastomeric Beams With Geometric Uncertainty
Authors: Hertlein Nathan , Yoo David , Buskohl Philip R. , Vemaganti Kumar , Anand Sam 
Published: November 2020

Abstract
Additive manufacturing has enabled the fabrication of complex, architected materials, which have shown great promise in fields such as acoustics, mechanical logic gates, and energy trapping, due to their unique properties derived from repeating unit cells. The force-displacement performance of one such unit cell, the bistable elastomeric beam, has been characterized experimentally and subsequently tuned by the introduction of a Fourier series-based design parameterization that enables a wider range of available energy performance characteristics and secondary stable configurations. Here, another characteristic of this beam that has not yet been explored, namely the shape during post-buckling deformation between the two stable states, is optimized under the same Fourier series-based parameterization. Nonlinear finite element analysis reveals that the performance is highly sensitive to even modest profile error incurred on the beam’s upper and lower sides during manufacturing. Various methods of quantifying performance are compared, and Bayesian optimization is employed in two case studies to achieve desired post-buckled shapes. A novel acquisition function, which considers a candidate design’s robustness to profile error, is used to find the design that achieves the desired performance consistently, even in the face of the variability associated with additive manufacturing. Finally, Monte Carlo simulations are used to quantify the performance of optimal beams found with and without the new acquisition function, and reveal the importance of considering geometric uncertainty during the optimization process.
Off-Road Vehicle Path Planning Using Geodesics on a Multifactor Terrain Model
Authors: Barthlow Dakota , Pandey Vijitashwa , Gorsich David , Jayakumar Paramsothy 
Published: November 2020

Abstract
Optimal navigation of wheeled or tracked vehicles through a particular off-road terrain is primarily governed by terrain properties, and the capabilities of the vehicle itself. Reconciling vehicle operation with a terrain’s trafficability, termed mobility mapping, is a complex and multi-faceted problem that involves geophysics, vehicle dynamics, optimization, meta-modeling, and statistical modeling. A mobility map in turn informs path planning, which is the process of creating optimal routes through the trafficable areas to successfully arrive at a destination. This optimality can be in the sense of the length of the path taken, energy consumption, or any other metric that the operator considers important. This paper presents a procedure that first models the terrain by including factors affecting trafficability, uses a kriging interpolator for terrain modeling, then utilizes an existing path planning algorithm to create a rough path between start and goal points. Subsequently, a differential geometry based algorithm is presented to optimize the path. In the proposed method, the height of the terrain is augmented with multiple factors beneficial or detrimental to mobility to define a composite surface, thereby simultaneously considering them in path planning. A geodesic connecting the start and goal points is then found on this composite surface. We present examples on terrains acquired from geospatial data gateway of the United States Geological Survey, showing the efficacy of the method. Comparisons with an existing approach are made and avenues for future work are also identified.
Validation of an Analytical Model to Lower the Cost of Solar-Powered Drip Irrigation Systems for Smallholder Farmers in the Mena Region
Authors: Grant Fiona , Sheline Carolyn , Amrose Susan , Brownell Elizabeth , Nangia Vinay , Talozi Samer , Winter Amos 
Published: November 2020

Abstract
Drip irrigation is a micro-irrigation technology that has been shown to conserve water and significantly increase crop yield. This technology could be particularly beneficial to the world’s estimated 500 million smallholder farmers, but drip systems tend to be financially inaccessible to this population. Drip systems require costly components including a pipe network, emitters, a pump and power system. Due to limited access to electricity, many smallholder farmers would require off-grid solutions. Designing reliable, low cost, off-grid drip irrigation systems for smallholder farms could significantly reduce the barrier to adoption.
This paper builds on an integrated solar-powered drip irrigation model that was shown to improve upon an existing software. Field trials of the small-scale drip system were conducted on research farms in Jordan and Morocco for a full growing season. Data collected from these field trials are used to validate the hydraulics portion of the systems-level model. In addition, the insights gained from the field trials were formed into design requirements for future iterations of the model. These include optimizing for the system life cycle cost, as opposed to capital cost, the ability to simulate the system operation over a season, the capability to input a user’s irrigation schedule, incorporating locally-available components, and incorporating a system reliability constraint based on more detailed agronomic calculations.
Pilot Study: Investigating EEG Based Neuro-Responses of Engineers via a Modified Alternative Uses Task to Understand Creativity
Authors: Hartog Tess , Marshall Megan , Ahad Md Tanvir , Alhashim Amin G. , Okudan Kremer Gul , van Hell Janet , Siddique Zahed 
Published: November 2020

Abstract
Assessing creativity is not an easy task, but that has not stopped researchers from exploring it. Because creativity is essential to engineering disciplines, knowing how to enhance creative abilities through engineering education has been a topic of interest. In this paper, the event related potential (ERP) technique is used to study the neural responses of engineers via a modified alternative uses task (AUT). Though only a pilot study testing two participants, the preliminary results of this study indicate general neuro-responsiveness to novel or unusual stimuli. These findings also suggest that a scaled-up study along these lines would enable better understanding and modeling of neuroresponses of engineers and creative thinking, as well as contribute to the growing field of ERP research in the field of engineering.
Hybrid Modeling Approach for Melt Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing
Authors: Moges Tesfaye , Yang Zhuo , Jones Kevontrez , Feng Shaw , Witherell Paul , Lu Yan 
Published: November 2020

Abstract
Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process-structure-property-performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser powder bed fusion (L-PBF) processes. Our unbiased, model integration method combines physics-based data and measurement data for approaching more accurate prediction of melt-pool width. Both a high-fidelity computational fluid dynamics (CFD) model and experiments utilizing optical images are used to generate a combined dataset of melt-pool widths. From this aggregated dataset, a hybrid model is developed using data-driven modeling techniques, including polynomial regression and Kriging methods. The performance of the hybrid model is evaluated by computing the average relative error and comparing it with the results of the simulations and surrogate models constructed from the original CFD model and experimental measurements. It is found that the proposed hybrid model performs better in terms of prediction accuracy and computational time. Future work includes a conceptual introduction on the use of an AM ontology to support improved model and data selection when constructing hybrid models. This study can be viewed as a significant step towards the use of hybrid models as predictive models with improved accuracy and without the sacrifice of speed.
Comparing Segmentation Approaches for Learning-Aware Wireframe Generation on Human Model
Authors: Huang Jida , Kwok Tsz-Ho 
Published: November 2020

Abstract
Wireframe has been proved very useful for learning human body from semantic parameters. However, the definition of the wireframe is highly dependent on the anthropological experiences of experts in previous works. Hence it is usually not easy to obtain a well-defined wireframe for a new set of human models in the available database. To overcome such difficulty, an automated wireframe generation method would be very helpful in relieving the need for manual anthropometric definition. In order to find such an automated wireframe designing method, a natural way is using automatic segmentation methods to divide the human body model into small mesh patches. Nevertheless, different segmentation approaches could have various segmented patches, thus resulting in various wireframes. How these wireframes affect human body learning performance? In this paper, we attempt to answer this research question by comparing different segmentation methods. Different wireframes are generated with the mesh segmentation methods, and then we use these wireframes as an intermediate agent to learn the relationship between the human body mesh models and the semantic parameters. We compared the reconstruction accuracy with different generated wireframe sets and summarized several meaningful design guidelines for developing an automatic wireframe-aware segmentation method for human body learning.