Real-time monitoring of the rubber belt tension in an industrial conveyor

The paper presents a novel system for monitoring of the work of industrial belt conveyor. It is based on the strain gauges placed directly on the roller surface that measure pressing force of the belt on the roller. Automatical operation of the measurement system minimizes impact of an operator on the measurement results. Experimental researches included the stability of indications during 5 days, Type A uncertainty estimation and equipment variation EV calculations. Expanded uncertainty calculated for the level of confidence 95% was below 0.1% of the actually measured value, and percentage repeatability %EV = 9.5% was obtained. It can be considered satisfactory, since usually it is required %EV < 10% for new measurement systems.

Real-time monitoring of the rubber belt tension in an industrial conveyor

The paper presents a novel system for monitoring of the work of industrial belt conveyor. It is based on the strain gauges placed directly on the roller surface that measure pressing force of the belt on the roller. Automatical operation of the measurement system minimizes impact of an operator on the measurement results. Experimental researches included the stability of indications during 5 days, Type A uncertainty estimation and equipment variation EV calculations. Expanded uncertainty calculated for the level of confidence 95% was below 0.1% of the actually measured value, and percentage repeatability %EV = 9.5% was obtained. It can be considered satisfactory, since usually it is required %EV < 10% for new measurement systems.

A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective

Decision-making based on machine learning systems, especially when this decision-making can affect human lives, is a subject of maximum interest in the Machine Learning community. It is, therefore, necessary to equip these systems with a means of estimating uncertainty in the predictions they emit in order to help practitioners make more informed decisions. In the present work, we introduce the topic of uncertainty estimation, and we analyze the peculiarities of such estimation when applied to classification systems. We analyze different methods that have been designed to provide classification systems based on deep learning with mechanisms for measuring the uncertainty of their predictions. We will take a look at how this uncertainty can be modeled and measured using different approaches, as well as practical considerations of different applications of uncertainty. Moreover, we review some of the properties that should be borne in mind when developing such metrics. All in all, the present survey aims at providing a pragmatic overview of the estimation of uncertainty in classification systems that can be very useful for both academic research and deep learning practitioners.

Pyramid Bayesian Method for Model Uncertainty Evaluation of Semantic Segmentation in Autonomous Driving

With the fast-developing deep learning models in the field of autonomous driving, the research on the uncertainty estimation of deep learning models has also prevailed. Herein, a pyramid Bayesian deep learning method is proposed for the model uncertainty evaluation of semantic segmentation. Semantic segmentation is one of the most important perception problems in understanding visual scene, which is critical for autonomous driving. This study to optimize Bayesian SegNet for uncertainty evaluation. This paper first simplifies the network structure of Bayesian SegNet by reducing the number of MC-Dropout layer and then introduces the pyramid pooling module to improve the performance of Bayesian SegNet. mIoU and mPAvPU are used as evaluation matrics to test the proposed method on the public Cityscapes dataset. The experimental results show that the proposed method improves the sampling effect of the Bayesian SegNet, shortens the sampling time, and improves the network performance.

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks

Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user’s input, hence providing crucial information about its closeness to skin lesion images from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org.