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Implementing circularity measurements in industry 4.0-based manufacturing metrology using MQTT protocol and Open CV: A case study
In the context of Industry 4.0, manufacturing metrology is crucial for inspecting and measuring machines. The Internet of Things (IoT) technology enables seamless communication between advanced industrial devices through local and cloud computing servers. This study investigates the use of the MQTT protocol to enhance the performance of circularity measurement data transmission between cloud servers and round-hole data sources through Open CV. Accurate inspection of circular characteristics, particularly roundness errors, is vital for lubricant distribution, assemblies, and rotational force innovation. Circularity measurement techniques employ algorithms like the minimal zone circle tolerance algorithm. Vision inspection systems, utilizing image processing techniques, can promptly and accurately detect quality concerns by analyzing the model’s surface through circular dimension analysis. This involves sending the model’s image to a computer, which employs techniques such as Hough Transform, Edge Detection, and Contour Analysis to identify circular features and extract relevant parameters. This method is utilized in the camera industry and component assembly. To assess the performance, a comparative experiment was conducted between the non-contact-based 3SMVI system and the contact-based CMM system widely used in various industries for roundness evaluation. The CMM technique is known for its high precision but is time-consuming. Experimental results indicated a variation of 5 to 9.6 micrometers between the two methods. It is suggested that using a high-resolution camera and appropriate lighting conditions can further enhance result precision.
Journal Article
Estimating animal density using the Space‐to‐Event model and bootstrap resampling with motion‐triggered camera‐trap data
Abstract Over the past few decades, the use of camera‐traps has revolutionized our ability to monitor populations of wild terrestrial mammals. While methods to estimate abundance from individually‐identifiable animals are well‐established, they are mostly restricted to species with clear natural markings or else necessitate invasive and often costly animal tagging campaigns. Estimating abundance or density from unmarked animals remains challenging. Several models recently developed to deal with this issue are promising, but are not widely used by field ecologists. Here, we developed a framework for applying the Space‐To‐Event (STE) model—originally designed to be used with time‐lapse images—on motion‐triggered camera‐trap data. Our approach involves performing bootstrap resampling on the photographic dataset to generate multiple datasets that are then used as input to the STE model. We tested our approach on 29 datasets, including 17 ungulate species from eight sites, in six different countries and various ecosystems. Then, we conducted a regression analysis to evaluate how variations in ecological and sampling conditions across studies affected the bias and precision of our STE density estimates. Our study shows that with a bootstrap resampling approach and information on animal activity and effective detection distances to animals, the STE model can be used to analyze motion‐trigger datasets and provide population density estimates that are similar to those from other methods. We found that measuring the camera viewshed was critical to prevent major negative biases in density estimates. Moreover, using a 1‐s sampling window was important to avoid the positive bias that results from violating the instantaneous‐sampling assumption. We found that precision increased with greater sampling effort and higher density populations. Based on these results, we highlight several issues from past studies that have applied the original timelapse‐based STE to motion‐trigger datasets, issues that our bootstrap resampling approach addresses. We caution that the STE model, whether applied to timelapse or motion‐triggered datasets, relies on strict assumptions. Any violations of these assumptions, such as non‐instantaneous sampling or the application of angle and distance of detection provided by the camera manufacturer, can cause biases in multiple directions that may be difficult to differentiate.
Journal Article
Early entrants attract better customer evaluations: evidence from the digital camera industry
PurposeIn addition to pioneering, empirical work on entry order increasingly addresses fast followers and laggards and the potential advantages they are able to capture. There is also a growing consensus in the academia, that current measures of firm performance used in the entry order literature to study these advantages are inadequate. This study analyzes the relationship between entry order and customer evaluations, which, depicting the performance of the firm's products in the market, are used as a proxy for firm performance.Design/methodology/approachThe study is set in the digital camera industry, analyzing entries into each new technology level, in terms of the sensor resolution of compact and bridge cameras. The complete dataset consisted of 1,816 digital camera models introduced between January 1996 and December 2017. The data are analyzed using hierarchical multiple linear regression.FindingsThe study finds evidence of early-mover advantage for the compact product category. In the compact camera consumer market, both first-movers and fast followers outperform late movers. Furthermore, the difference in performance in comparison to laggards is greater for first-movers than for fast followers. However, in the bridge category which consists of a more heterogeneous set of products, no significant entry-order effects are detected.Originality/valueThe results clearly indicate that there exists an early mover advantage. Furthermore, the results are not consistent across different product categories within an industry; hence, caution needs to be exercised when analyzing industry dynamics and entry order effects. Finally, our novel conceptualization of firm performance measured as online customer evaluation add new opportunities to investigate firm success
Journal Article
Learning by Doing and the Demand for Advanced Products
Consumer learning by doing builds general and product-specific human capital, shaping their adoption patterns for advanced products.
How much does consumer learning by doing affect the demand for advanced products? In the context of digital cameras, I use detailed picture-level data to directly measure changes in picture quality as a result of learning by doing or product switching. Although learning by doing builds up consumer human capital, a fraction of this human capital is product specific, creating consumer switching costs. To quantify the role of consumer human capital, I structurally estimate the demand for digital cameras with consumer learning by doing. The evolution of consumer human capital explains 23% of the sales of advanced digital cameras, whereas brand-specific human capital—arising from incompatibility in product design—explains 15% of consumer brand-choice inertia.
Data and the online appendix are available at
https://doi.org/10.1287/mksc.2018.1118
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Journal Article
Leaf Physiological Responses and Early Senescence Are Linked to Reflectance Spectra in Salt-Sensitive Coastal Tree Species
Salt-sensitive trees in coastal wetlands are dying as forests transition to marsh and open water at a rapid pace. Forested wetlands are experiencing repeated saltwater exposure due to the frequency and severity of climatic events, sea-level rise, and human infrastructure expansion. Understanding the diverse responses of trees to saltwater exposure can help identify taxa that may provide early warning signals of salinity stress in forests at broader scales. To isolate the impacts of saltwater exposure on trees, we performed an experiment to investigate the leaf-level physiology of six tree species when exposed to oligohaline and mesohaline treatments. We found that species exposed to 3–6 parts per thousand (ppt) salinity had idiosyncratic responses of plant performance that were species-specific. Saltwater exposure impacted leaf photochemistry and caused early senescence in Acer rubrum, the most salt-sensitive species tested, but did not cause any impacts on plant water use in treatments with <6 ppt. Interestingly, leaf spectral reflectance was correlated with the operating efficiency of photosystem II (PSII) photochemistry in A. rubrum leaves before leaf physiological processes were impacted by salinity treatments. Our results suggest that the timing and frequency of saltwater intrusion events are likely to be more detrimental to wetland tree performance than salinity concentrations.
Journal Article