This paper introduces the design principle of legibility as means to examine the epistemic and ethical conditions of sensing technologies. Emerging sensing technologies create new possibilities regarding what to measure, as well as how to analyze, interpret, and communicate said measurements. In doing so, they create ethical challenges for designers to navigate, specifically how the interpretation and communication of complex data affect moral values such as (user) autonomy. Contemporary sensing technologies require layers of mediation and exposition to render what they sense as intelligible and constructive to the end user, which is a value-laden design act. Legibility is positioned as both an evaluative lens and a design criterion, making it complimentary to existing frameworks such as value sensitive design. To concretize the notion of legibility, and understand how it could be utilized in both evaluative and anticipatory contexts, the case study of a vest embedded with sensors and an accompanying app for patients with chronic obstructive pulmonary disease is analyzed.
In this work, a feasible and low-cost approach is proposed for level measurement in multiphase systems inside tanks used for petroleum-derived oil production. The developed level sensor system consisted of light-emitting diodes (LEDs), light-dependent resistor (LDR), and a low-cost microprocessor. Two different types of oil were tested: AW460 and AW68. Linear regression (LR) was applied for 11 scenarios and showed a direct correlation between the level of oil and the sensor’s output. The measurement with AW460 oil presented a perfect linear behavior, while for AW68, a higher standard deviation was obtained justifying the occurrence of the nonlinearity in several scenarios. In order to overcome the nonlinear effect, two machine learning (ML) techniques were tested: K-nearest neighbors regression (KNNR) and multilayer perceptron (MLP) neural network regression. The highest correlation coefficient ( R2 ) and the lowest root mean squared error (RMSE) were obtained for AW68 with MLP. Therefore, MLP was used for regression (level prediction for water, oil, and emulsion) as well as classification (identify the type of oil in the reservoir) simultaneously. The suggested network exhibited a high accuracy for oil identification (99.801%) and improved linear performance in regression ( R2 = 0.9989 and RMSE = 0.065).
From the article: "A facile approach for the fabrication of large-scale interdigitated nanogap electrodes (nanogap IDEs) with a controllable gap was demonstrated with conventional micro-fabrication technology to develop chemocapacitors for gas sensing applications. In this work, interdigitated nanogap electrodes (nanogap IDEs) with gaps from 50–250 nm have been designed and processed at full wafer-scale. These nanogap IDEs were then coated with poly(4-vinyl phenol) as a sensitive layer to form gas sensors for acetone detection at low concentrations. These acetone sensors showed excellent sensing performance with a dynamic range from 1000 ppm to 10 ppm of acetone at room temperature and the observed results are compared with conventional interdigitated microelectrodes according to our previous work. Sensitivity and reproducibility of devices are discussed in detail. Our approach of fabrication of nanogap IDEs together with a simple coating method to apply the sensing layer opens up possibilities to create various nanogap devices in a cost-effective manner for gas sensing applications"
MULTIFILE
