A safe and energy efficient robotic system for industrial automatic tests on domestic appliances: Problem statement and proof of concept

In this paper, the design and the development of a robotic platform conceived to perform accelerated life tests on a newly manufactured domestic appliances is presented. The proposed system aims at improving the safety of human operators that share the workspace with the robotic platform which is a common scenario of test laboratories. A deep learning algorithm is used for the human detection and pose estimation, while the integration between a conventional motion planning algorithm with a fast 3D collision checker has been implemented as a global planner plugin for the ROS navigation stack. With the twofold objective of improving safety and saving energy in the battery-powered mobile manipulator used in this project, the problem of minimizing the overall kinetic energy is addressed through a properly designed task priority controller, in which the manipulator inertia matrix is used to weight the joint speeds while satisfying multiple robotic tasks according to a hierarchy designed to interact with the appliances while preserving the safety of the human operators. Simulations are carried out to evaluate the overall control architecture and preliminary results indicate the effectiveness of the developed system in the test laboratory floors

Deep Learning for Black-Box Modeling of Audio Effects

Virtual analog modeling of audio effects consists of emulating the sound of an audio processor reference device. This digital simulation is normally done by designing mathematical models of these systems. It is often difficult because it seeks to accurately model all components within the effect unit, which usually contains various nonlinearities and time-varying components. Most existing methods for audio effects modeling are either simplified or optimized to a very specific circuit or type of audio effect and cannot be efficiently translated to other types of audio effects. Recently, deep neural networks have been explored as black-box modeling strategies to solve this task, i.e., by using only input–output measurements. We analyse different state-of-the-art deep learning models based on convolutional and recurrent neural networks, feedforward WaveNet architectures and we also introduce a new model based on the combination of the aforementioned models. Through objective perceptual-based metrics and subjective listening tests we explore the performance of these models when modeling various analog audio effects. Thus, we show virtual analog models of nonlinear effects, such as a tube preamplifier; nonlinear effects with memory, such as a transistor-based limiter and nonlinear time-varying effects, such as the rotating horn and rotating woofer of a Leslie speaker cabinet

A History of Audio Effects

Audio effects are an essential tool that the field of music production relies upon. The ability to intentionally manipulate and modify a piece of sound has opened up considerable opportunities for music making. The evolution of technology has often driven new audio tools and effects, from early architectural acoustics through electromechanical and electronic devices to the digitisation of music production studios. Throughout time, music has constantly borrowed ideas and technological advancements from all other fields and contributed back to the innovative technology. This is defined as transsectorial innovation and fundamentally underpins the technological developments of audio effects. The development and evolution of audio effect technology is discussed, highlighting major technical breakthroughs and the impact of available audio effects

Computer Simulation of a Nitric Oxide-Releasing Catheter with a Novel Stable Convection-Diffusion Equation Solver and Automatic Quantification of Lung Ultrasound Comets by Machine Learning

Biological transport processes often involve a boundary acting as separation of flow, most commonly in transport involving blood-contacting medical devices. The separation of flow creates two different scenarios of mass transport across the interface. No flow exists within the medical device and diffusion governs mass transport; both convection and diffusion exist when flow is present. The added convection creates a large concentration gradient around the interface. Computer simulation of such cases prove to be difficult and require proper shock capturing methods for the solutions to be stable, which is typically lacking in commercial solvers. In this thesis, we propose a second-order accurate numerical method for solving the convection-diffusion equation by using a gradient-limited Godunov-type convective flux and the multi-point flux approximation (MPFA) L-Method for the diffusion flux. We applied our solver towards simulation of a nitric oxide-releasing intravascular catheter. Intravascular catheters are essential for long-term vascular access in both diagnosis and treatment. Use of catheters are associated with risks for infection and thrombosis. Because infection and thrombosis lead to impaired flow and potentiality life threatening systemic infections, this leads to increased morbidity and mortality, requiring catheters to be replaced among other treatments for these complications. Nitric oxide (NO) is a potent antimicrobial and antithrombotic agent produced by vascular endothelial cells. The production level in vivo is so low that the physiological effects can only be seen around the endothelial cells. The catheter can incorporate a NO source in two major ways: by impregnating the catheter with NO-releasing compounds such as S-nitroso-N-acetyl penicillamine (SNAP) or using electrochemical reactions to generate NO from nitrites. We applied our solver to both situations to guide the design of the catheter. Simulations revealed that dissolved NO inside the catheter is depleted after 12 minutes without resupplying, and electrochemical release of NO requires 10.5 minutes to reach steady state. Lung edema is often present in patients with end-stage renal disease due to reduced filtration functions of the kidney. These patients require regular dialysis sessions to manage their fluid status. The clinical gold standard to quantify lung edema is to use CT, which exposes patients to high amounts of radiation and is not cost efficient. Fluid management in such patients becomes very challenging without a clear guideline of fluid to be removed during dialysis sessions. Hypotension during dialysis can limit fluid removal, even in the setting of ongoing fluid overload or congestive heart failure. Accurate assessment of the pulmonary fluid status is needed, so that fluid overload and congestive heart failure can be detected, especially in the setting of hypotension, allowing dialysis to be altered to improve fluid removal. Recently, reverberations in ultrasound signals, referred to as ``lung comets'' have emerged as a potential quantitative way to measure lung edema. Increased presence of lung comets is associated with higher amounts of pulmonary edema, higher mortality, and more adverse cardiac events. However, the lung comets are often counted by hand by physicians with single frames in lung ultrasound and high subjectivity has been found to exist among the counting by physicians. We applied image processing and neural network techniques as an attempt to provide an objective and accurate measurement of the amount of lung comets present. Our quantitative results are significantly correlated with diastolic blood pressure and ejection fraction.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163182/1/micw_1.pd

Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version