212 research outputs found
Exploring acceptance of autonomous vehicle policies using KeyBERT and SNA: Targeting engineering students
This study aims to explore user acceptance of Autonomous Vehicle (AV)
policies with improved text-mining methods. Recently, South Korean policymakers
have viewed Autonomous Driving Car (ADC) and Autonomous Driving Robot (ADR) as
next-generation means of transportation that will reduce the cost of
transporting passengers and goods. They support the construction of V2I and V2V
communication infrastructures for ADC and recognize that ADR is equivalent to
pedestrians to promote its deployment into sidewalks. To fill the gap where
end-user acceptance of these policies is not well considered, this study
applied two text-mining methods to the comments of graduate students in the
fields of Industrial, Mechanical, and Electronics-Electrical-Computer. One is
the Co-occurrence Network Analysis (CNA) based on TF-IWF and Dice coefficient,
and the other is the Contextual Semantic Network Analysis (C-SNA) based on both
KeyBERT, which extracts keywords that contextually represent the comments, and
double cosine similarity. The reason for comparing these approaches is to
balance interest not only in the implications for the AV policies but also in
the need to apply quality text mining to this research domain. Significantly,
the limitation of frequency-based text mining, which does not reflect textual
context, and the trade-off of adjusting thresholds in Semantic Network Analysis
(SNA) were considered. As the results of comparing the two approaches, the
C-SNA provided the information necessary to understand users' voices using
fewer nodes and features than the CNA. The users who pre-emptively understood
the AV policies based on their engineering literacy and the given texts
revealed potential risks of the AV accident policies. This study adds
suggestions to manage these risks to support the successful deployment of AVs
on public roads.Comment: 29 pages with 11 figure
Price Discrimination with Demarketing
We study how demarketing interacts with pricing decisions to explain why and when it can be employed as the seller's optimal strategy. In our model, a monopolistic seller offers different price-quality bundles of the product. A consumer's preference is private information. With demarketing, consumers must make a costly effort to purchase and/or utilize the product, whereas with marketing, the seller instead makes the effort so that the consumer's purchasing decision is independent of the cost of effort. Our result suggests that, for small or large effort costs, it is optimal for the seller to engage in marketing. For intermediate effort costs, however, demarketing can be optimal. With demarketing, the seller induces only the consumers with high valuation to make transaction effort. By doing so, the seller can price discriminate more effectively, thus extracting more surplus. We extend our analysis to the case where the seller can offer special deals through exclusive sales channels along with demarketing. Then, demarketing can be optimal even for large costs of effort
Dynamic Control of Adsorption Sensitivity for Photo-EMF-Based Ammonia Gas Sensors Using a Wireless Network
This paper proposes an adsorption sensitivity control method that uses a wireless network and illumination light intensity in a photo-electromagnetic field (EMF)-based gas sensor for measurements in real time of a wide range of ammonia concentrations. The minimum measurement error for a range of ammonia concentration from 3 to 800 ppm occurs when the gas concentration magnitude corresponds with the optimal intensity of the illumination light. A simulation with LabView-engineered modules for automatic control of a new intelligent computer system was conducted to improve measurement precision over a wide range of gas concentrations. This gas sensor computer system with wireless network technology could be useful in the chemical industry for automatic detection and measurement of hazardous ammonia gas levels in real time
Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model
Road infrastructure can affect the occurrence of road accidents. Therefore,
identifying roadway features with high accident probability is crucial. Here,
we introduce image inpainting that can assist authorities in achieving safe
roadway design with minimal intervention in the current roadway structure.
Image inpainting is based on inpainting safe roadway elements in a roadway
image, replacing accident-prone (AP) features by using a diffusion model. After
object-level segmentation, the AP features identified by the properties of
accident hotspots are masked by a human operator and safe roadway elements are
inpainted. With only an average time of 2 min for image inpainting, the
likelihood of an image being classified as an accident hotspot drops by an
average of 11.85%. In addition, safe urban spaces can be designed considering
human factors of commuters such as gaze saliency. Considering this, we
introduce saliency enhancement that suggests chrominance alteration for a safe
road view.Comment: 9 Pages, 6 figures, 4 table
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