scholarly journals A Comparison of Two Sensory Panels Trained with Different Feedback Calibration Range Specifications via Sensory Description of Five Beers

Foods ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 534 ◽  
Author(s):  
Line Elgaard ◽  
Line A. Mielby ◽  
Helene Hopfer ◽  
Derek V. Byrne
Keyword(s):  
Average Distance ◽  
Calibration Method ◽  
The Self ◽  
Use Of Self ◽  
Fixed Target ◽  
Immediate Feedback ◽  
Leader Following ◽  
Novel Method ◽  
Standard Deviations ◽  
Sensory Profiles

Feedback on panel performance is traditionally provided by the panel leader, following an evaluation session. However, a novel method for providing immediate feedback to panelists was proposed, the Feedback Calibration Method (FCM). The aim of the current study was to compare the performance of two panels trained by using FCM with two different approaches for ranges calibration, namely self-calibrated and fixed ranges. Both panels were trained using FCM for nine one-hour sessions, followed by a sensory evaluation of five beer samples (in replicates). Results showed no difference in sample positioning in the sensory space by the two panels. Furthermore, the panels’ discriminability was also similar, while the self-calibrated panel had the highest repeatability. The results from the average distance from target and standard deviations showed that the self-calibrated panel had the lowest distance from target and standard deviation throughout all sessions. However, the decrease in average distance from target and standard deviations over training sessions was similar among panels, meaning that the increase in performance was similar. The fact that both panels had a similar increase in performance and yielded similar sensory profiles indicates that the choice of target value calibration method is unimportant. However, the use of self-calibrated ranges could introduce an issue with the progression of the target scores over session, which is why the fixed target ranges should be applied, if available.

scholarly journals SHAPE FUNCTION-BASED ESTIMATION OF DEFORMATION WITH MOVING CAMERAS ATTACHED TO THE DEFORMING BODY

2016 ◽  
Vol III-5 ◽  
pp. 11-17
Author(s):  
O. Jokinen ◽  
I. Ranta ◽  
H. Haggrén ◽  
P. Rönnholm
Keyword(s):  
Shape Function ◽  
Average Distance ◽  
The Self ◽  
Frame Structure ◽  
Multiple Cameras ◽  
Single Image ◽  
Shape Functions ◽  
Imaging Geometry ◽  
Novel Method ◽  
Physical Limitations

The paper presents a novel method to measure 3-D deformation of a large metallic frame structure of a crane under loading from one to several images, when the cameras need to be attached to the self deforming body, the structure sways during loading, and the imaging geometry is not optimal due to physical limitations. The solution is based on modeling the deformation with adequate shape functions and taking into account that the cameras move depending on the frame deformation. It is shown that the deformation can be estimated even from a single image of targeted points if the 3-D coordinates of the points are known or have been measured before loading using multiple cameras or some other measuring technique. The precision of the method is evaluated to be 1 mm at best, corresponding to 1:11400 of the average distance to the target.

scholarly journals SHAPE FUNCTION-BASED ESTIMATION OF DEFORMATION WITH MOVING CAMERAS ATTACHED TO THE DEFORMING BODY

2016 ◽  
Vol III-5 ◽  
pp. 11-17
Author(s):  
O. Jokinen ◽  
I. Ranta ◽  
H. Haggrén ◽  
P. Rönnholm
Keyword(s):  
Shape Function ◽  
Average Distance ◽  
The Self ◽  
Frame Structure ◽  
Multiple Cameras ◽  
Single Image ◽  
Shape Functions ◽  
Imaging Geometry ◽  
Novel Method ◽  
Physical Limitations

The paper presents a novel method to measure 3-D deformation of a large metallic frame structure of a crane under loading from one to several images, when the cameras need to be attached to the self deforming body, the structure sways during loading, and the imaging geometry is not optimal due to physical limitations. The solution is based on modeling the deformation with adequate shape functions and taking into account that the cameras move depending on the frame deformation. It is shown that the deformation can be estimated even from a single image of targeted points if the 3-D coordinates of the points are known or have been measured before loading using multiple cameras or some other measuring technique. The precision of the method is evaluated to be 1 mm at best, corresponding to 1:11400 of the average distance to the target.

Expressing the Self

2018 ◽  
Keyword(s):  
Philosophy Of Mind ◽  
The Self ◽  
Epistemic Status ◽  
First Person ◽  
Semantic Categories ◽  
Wide Range ◽  
Person Reference ◽  
De Se ◽  
Novel Method ◽  
Cultural Pragmatics

This book addresses different linguistic and philosophical aspects of referring to the self in a wide range of languages from different language families, including Amharic, English, French, Japanese, Korean, Mandarin, Newari (Sino-Tibetan), Polish, Tariana (Arawak), and Thai. In the domain of speaking about oneself, languages use a myriad of expressions that cut across grammatical and semantic categories, as well as a wide variety of constructions. Languages of Southeast and East Asia famously employ a great number of terms for first-person reference to signal honorification. The number and mixed properties of these terms make them debatable candidates for pronounhood, with many grammar-driven classifications opting to classify them with nouns. Some languages make use of egophors or logophors, and many exhibit an interaction between expressing the self and expressing evidentiality qua the epistemic status of information held from the ego perspective. The volume’s focus on expressing the self, however, is not directly motivated by an interest in the grammar or lexicon, but instead stems from philosophical discussions of the special status of thoughts about oneself, known as de se thoughts. It is this interdisciplinary understanding of expressing the self that underlies this volume, comprising philosophy of mind at one end of the spectrum and cross-cultural pragmatics of self-expression at the other. This unprecedented juxtaposition results in a novel method of approaching de se and de se expressions, in which research methods from linguistics and philosophy inform each other. The importance of this interdisciplinary perspective on expressing the self cannot be overemphasized. Crucially, the volume also demonstrates that linguistic research on first-person reference makes a valuable contribution to research on the self tout court, by exploring the ways in which the self is expressed, and thereby adding to the insights gained through philosophy, psychology, and cognitive science.

scholarly journals Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

2021 ◽  
Vol 11 (2) ◽  
pp. 582
Author(s):  
Zean Bu ◽  
Changku Sun ◽  
Peng Wang ◽  
Hang Dong
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Calibration Method ◽  
Multiple Sensors ◽  
Triangular Pyramid ◽  
World Coordinate System ◽  
Flash Lidar ◽  
Novel Method ◽  
3D Information ◽  
Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

scholarly journals An Automatic Offset Calibration Method for Differential Charge-Based Capacitance Measurement

2021 ◽  
Vol 11 (2) ◽  
pp. 22
Author(s):  
Umberto Ferlito ◽  
Alfio Dario Grasso ◽  
Michele Vaiana ◽  
Giuseppe Bruno
Keyword(s):  
Standard Deviation ◽  
Output Voltage ◽  
Process Variations ◽  
Calibration Method ◽  
Capacitance Measurement ◽  
Effective Technique ◽  
Fully Integrated ◽  
Front End ◽  
Novel Method ◽  
On Chip

Charge-Based Capacitance Measurement (CBCM) technique is a simple but effective technique for measuring capacitance values down to the attofarad level. However, when adopted for fully on-chip implementation, this technique suffers output offset caused by mismatches and process variations. This paper introduces a novel method that compensates the offset of a fully integrated differential CBCM electronic front-end. After a detailed theoretical analysis of the differential CBCM topology, we present and discuss a modified architecture that compensates mismatches and increases robustness against mismatches and process variations. The proposed circuit has been simulated using a standard 130-nm technology and shows a sensitivity of 1.3 mV/aF and a 20× reduction of the standard deviation of the differential output voltage as compared to the traditional solution.

scholarly journals Co-evolving with self-tracking technologies

2018 ◽  
Vol 20 (10) ◽  
pp. 3624-3640 ◽  
Author(s):  
Dorthe Brogård Kristensen ◽  
Minna Ruckenstein
Keyword(s):  
Political Economy ◽  
Digital Technology ◽  
The Self ◽  
Quantified Self ◽  
Use Of Self ◽  
Technology Market

Seen in a longitudinal perspective, Quantified Self-inspired self-tracking sets up “a laboratory of the self,” where people co-evolve with technologies. By exploring ways in which self-tracking technologies energize everyday aims or are experienced as limiting, we demonstrate how some aspects of the self are amplified while others become reduced and restricted. We suggest that further developing the concept of the laboratory of the self renews the conversation about the role of metrics and technologies by facilitating comparison between different realms of the digital, and demonstrating how services and devices enlarge aspects of the self at the expense of others. The use of self-tracking technologies is inscribed in, but also runs counter to, the larger political-economy landscape. Personal laboratories can aid the exploration of how the techno-mediated selves fit into larger structures of the digital technology market and the role that metrics play in defining them.

scholarly journals Topology assisted self-organization of colloidal nanoparticles: application to 2D large-scale nanomastering

2014 ◽  
Vol 5 ◽  
pp. 1203-1209 ◽  
Author(s):  
Hind Kadiri ◽  
Serguei Kostcheev ◽  
Daniel Turover ◽  
Rafael Salas-Montiel ◽  
Komla Nomenyo ◽  
...  
Keyword(s):  
Large Scale ◽  
The Self ◽  
Surface Topology ◽  
Self Organization ◽  
Hydrogen Silsesquioxane ◽  
Polystyrene Beads ◽  
Structure Topology ◽  
Self Organized ◽  
Novel Method ◽  
Long Range Ordering

Our aim was to elaborate a novel method for fully controllable large-scale nanopatterning. We investigated the influence of the surface topology, i.e., a pre-pattern of hydrogen silsesquioxane (HSQ) posts, on the self-organization of polystyrene beads (PS) dispersed over a large surface. Depending on the post size and spacing, long-range ordering of self-organized polystyrene beads is observed wherein guide posts were used leading to single crystal structure. Topology assisted self-organization has proved to be one of the solutions to obtain large-scale ordering. Besides post size and spacing, the colloidal concentration and the nature of solvent were found to have a significant effect on the self-organization of the PS beads. Scanning electron microscope and associated Fourier transform analysis were used to characterize the morphology of the ordered surfaces. Finally, the production of silicon molds is demonstrated by using the beads as a template for dry etching.

GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making

2018 ◽  
Vol 35 (14) ◽  
pp. 2458-2465 ◽  
Author(s):  
Johanna Schwarz ◽  
Dominik Heider
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
State Of The Art ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Calibration Method ◽  
Probability Estimates ◽  
Calibration Methods ◽  
Novel Method

Abstract Motivation Clinical decision support systems have been applied in numerous fields, ranging from cancer survival toward drug resistance prediction. Nevertheless, clinical decision support systems typically have a caveat: many of them are perceived as black-boxes by non-experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability-focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of this study were (i) to develop a tool for fast and comparative analysis of different calibration methods, (ii) to demonstrate their limitations for the use on clinical data and (iii) to introduce our novel method GUESS. Results We compared the performances of two different state-of-the-art calibration methods, namely histogram binning and Bayesian Binning in Quantiles, as well as our novel method GUESS on both, simulated and real-world datasets. GUESS demonstrated calibration performance comparable to the state-of-the-art methods and always retained accurate class discrimination. GUESS showed superior calibration performance in small datasets and therefore may be an optimal calibration method for typical clinical datasets. Moreover, we provide a framework (CalibratR) for R, which can be used to identify the most suitable calibration method for novel datasets in a timely and efficient manner. Using calibrated probability estimates instead of original classifier scores will contribute to the acceptance and dissemination of machine learning based classification models in cost-sensitive applications, such as clinical research. Availability and implementation GUESS as part of CalibratR can be downloaded at CRAN.

scholarly journals Does self-modulated learning vs. algorithm-regulated learning of dermatology morphology affect learning efficiency of medical students?

2019 ◽  
Vol 10 (3) ◽  
pp. e82-90
Author(s):  
Danya Traboulsi ◽  
Jori Hardin ◽  
Laurie Parsons ◽  
Jason Waechter
Keyword(s):  
Medical Students ◽  
Student Satisfaction ◽  
Skill Acquisition ◽  
Demographic Data ◽  
The Self ◽  
First Year ◽  
Immediate Feedback ◽  
Learning Efficiency ◽  
Post Test ◽  
First Year Medical Students

Background: Deliberate practice is an important method of skill acquisition and is under-utilized in dermatology training. We delivered a dermatologic morphology training module with immediate feedback for first year medical students. Our goal was to determine whether there are differences in accuracy and learning efficiency between self-regulated and algorithm-regulated groups. Methods: First year medical students at the University of Calgary completed a dermatologic morphology module. We randomly assigned them to either a self-regulated arm (students removed cases from the practice pool at their discretion) or an algorithm-regulated arm (an algorithm determined when a case would be removed). We then administered a pre-survey, pre-test, post-test, and post-survey. Data collected included mean diagnostic accuracy of the practice sessions and tests, and the time spent practicing. The surveys assessed demographic data and student satisfaction. Results: Students in the algorithm-regulated arm completed more cases than the self-regulated arm (52.9 vs. 29.3, p<0.001) and spent twice as much time completing the module than the self-regulated participants (34.3 vs. 17.0 min., p<0.001). Mean scores were equivalent between the algorithm- and self-regulated groups for the pre-test (63% vs. 66%, n = 54) and post-test (90% vs. 86%, n = 10), respectively. Both arms demonstrated statistically significant improvement in the post-test. Conclusion: Both the self-regulated and algorithm-regulated arms improved at post-test. Students spent significantly less time practicing in the self-directed arm, suggesting it was more efficient.

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