Close encounters : spatial distances between people and a robot of mechanistic appearance

Peer reviewe

Specification and verification of radiation therapy system with respiratory compensation using Uppaal

The goal of radiation therapy is to give as much dose as possible to the target volume of tissue and avoid giving any dose to a healthy tissue. Advances of the digital control allow performing accurate plans and treatments. Unfortunately, motion compensation during the treatment remains a considerable problem. Currently, a combination of the different techniques, such as gating (restricting movement of patient) and periodic emission are used to avoid damaging healthy tissue. This paper focuses on systems that completely compensate respiratory movement (up to certain limit) and start by investigating adequacy of the existing hardware and software platform. In this paper a radiation therapy system consisting of a HexaPOD couch with 6-degrees movement, a tracking camera, a marker (markers) and a controller is modeled. A formal un-timed model was evaluated and found to be insufficient to completely determine adequacy of the system to compensate respiratory motion. Therefore, un-timed model was extended to include time and investigated. It provides more information than un-timed model, but does not answer all interesting question. Therefore, based on the results further research directions are sketched

Creep behavior of copper-chromium in-situ composite

Creep deformation and fracture behaviors were investigated on a deformation-processed Cu-Cr in-situ composite over a temperature range of 200 °C to 650 °C. It was found that the creep resistance increases significantly with the introduction of Cr fibers into Cu. The stress exponent and the activation energy for creep of the composite at high temperatures (≥400 °C) were observed to be 5.5 and 180 to 216 kJ/mol, respectively. The observation that the stress exponent and the activation energy for creep of the composite at high temperatures (≥400 °C) are close to those of pure Cu suggests that the creep deformation of the composite is dominated by the deformation of the Cu matrix. The high stress exponent at low temperatures (200 °C and 300 °C) is thought be associated with the as-swaged microstructure, which contains elongated dislocation cells and subgrains that are stable and act as strong athermal obstacles at low temperatures. The mechanism of damage was found to be similar for all the creep tests performed, but the distribution and extent of damage were found to be very sensitive to the test temperature

Time Series Prediction Using Support Vector Machines, the Orthogonal and the Regularised Orthogonal Least Squares Algorithms

Generalisation properties of support vector machines, orthogonal least squares and other variants of the orthogonal least squares algorithms are studied in this paper. In particular the zero-order regularised orthogonal least squares algorithm that has been proposed in (Chen et al. 1996) and the first order regularised orthogonal least squares algorithm which can be obtained using the cost function support vector machines will be discussed. Simple noisy sine and sinx functions are used to show that overfitting in the orthogonal least squares algorithm can be greatly reduced if the free parameters of the algorithm are selected properly. Results on three chaotic time series show that the orthiogonal least squares algorithm is slightly inferior compared to the other three algorithms. However, the strength of the orthogonal least squares algorithm lies in the ability to obtain a very concise or parsimonious model and the algorithm has the fewest number of free parameters compared to the other algorithms

The Application of Image Recognition and Machine Learning to Capture Readings of Traditional Blood Pressure Devices: A Platform to Promote Population Health Management to Prevent Cardiovascular Diseases

Digital solutions for Blood Pressure Monitoring (or Telemonitoring) have sprouted in recent years, innovative solutions are often connected to the Internet of Things (IoT), with mobile health (mHealth) platform. However, clinical validity, technology cost and cross-platform data integration remain as the major barriers for the application of these solutions. In this paper, we present an IoT-based and AI-embedded Blood Pressure Telemonitoring (BPT) system, which facilitates home blood pressure monitoring for individuals. The highlights of this system are the machine learning techniques to enable automatic digits recognition, with F1 score of 98.5%; and the cloud-based portal developed for automated data synchronization and risk stratification. Positive feedbacks on trial implementation are received from three clinics. The overall system architecture, development of machine learning model in digit identification and cloud-based telemonitoring are addressed in this paper, alongside the followed implications

The Effects of Noise Reduction on the Prediction Accuracy of Time Series

A new iterative smoothing method based on the extended Kalman filter is introduced to smooth noisy chaotic time series. Two examples are given to illustrate the smoothing method. The smoothing method is then employed as a noise prior to identification and prediction. Three different prediction methods are introduced and the prediction performance is compared using three nonlinear examples. Superior predictive performance is obtained by the prediction method that employs the pre-processing step on the data

A Nonlinear Smoothing Algorithm for Chaotic and Non-Chaotic Time Series

A new NARMA based smoothing algorithm is introduced for chaotic and non-chaotic time series. The new algorithm employs a cross validation method to determine the smoother structure, requires very little user interaction and can be combined with wavelet thresholding to further enhance the noise reduction. Numerical examples are included to illustrate the application of the new algorithm

Nonlinear Fisher Discriminant Analysis Using a Minimum Squared Error Cost Function and the Orthogonal Least Squares Algorithm.

The nonlinear discriminant function obtained using a minimum squared error cost function can be shown to be directly related to the nonlinear Fisher discriminant. With the squared error cost function, the orthogonal least squares algorithm can be used to find a parsimonious description of the nonlinear discriminant function. Two simple classification techniques will be introduced and tested on a number of real and artificial data sets. The results show that the new classification technique can often perform favourably with other state of the art classification techniques

A New Direct Approach of Computing Multi-Step Ahead Predictions for Nonlinear Models

A new direct approach of computing multi-step ahead predictions for nonlinear time series is introduced. The covariance of the parameter estimates associated with, and the mean squared k-step ahead prediction errors of the new direct approach are smaller than those obtained using the conventional direct approach. Numerical examples are included to illustrate the application of the new direct approach