Semi-automatic Maintenance of Regression Models: an Application in the Steel Industry

Software applications used in the controlling and planning of production processes commonly make use of predictive statistical models. Changes in the process involve a more or less regular need for updating the prediction models on which the operational software applications are based. The objective of this article is • to provide information which helps to design semiautomatic systems for the maintenance of statistical prediction models and • to describe a proof-of-concept implementation in an industrial application. The system developed processes the production data and provides an easy-to-use interface to construct updated models and introduce them into a software application. The article presents the architecture of the maintenance system, with a description of the algorithms that cause the system’s functionality. The system developed was implemented for keeping up-to-date prediction models which are in everyday use in a steel plate mill in the planning of the mechanical properties of steel products. The conclusion of the results is that the semi-automatic approach proposed is competitive with fully automatic and manual approaches. The benefits include good prediction accuracy and decreased workload of the deployment of updated model versions

Using a Semi-autonomous Drone Swarm to Support Wildfire Management – A Concept of Operations Development Study

This paper provides insights into a human factors-oriented Concept of Operations (ConOps), which can be applied for future semi-autonomous drone swarms to support the management of wildfires. The results provide, firstly, an overview of the current practices to manage wildfires in Finland. Secondly, some of the current challenges and future visions about drone usage in a wildfire situation are presented. Third, a description of the key elements of the developed future ConOps for operating a drone swarm to support the combat of wildfires is given. The ConOps has been formulated based on qualitative research, which included a literature review, seven subject matter expert interviews and a workshop with 40 professionals in the domain. Many elements of this ConOps may also be applied to a variety of other swarm robotics operations than only wildfire management. Finally, as the development of the ConOps is still in its first stage, several further avenues for research and development are proposed

Arterial pulse waves measured with EMFi and PPG sensors and comparison of the pulse waveform spectral and decomposition analysis in healthy subjects

The purpose of this study is to show the time domain and frequency domain analysis of signals recorded with Electromechanical Film (EMFi) and Photoplethysmographic (PPG) sensors in arterial elasticity estimation via pulse wave decomposition and spectral components obtained from left forefinger, wrist, and second toe arteries. ECG and pulse waves from the subjects were recorded from 7 persons (30‐60 y) in supine position. Decomposition of the pulse waves produces five components: percussion, tidal, dicrotic, repercussion, and retidal waves. Pulse wave decomposition parameters between EMFi and PPG are compared to detect variables for information on person’s arterial elasticity. Results show that elasticity information in the form of pulse wave decomposition from PPG and EMFi waves is obtainable and shows clear shortening between percussion wave and tidal wave peak time in PPG waveforms with age. The spectral information obtained with frequency domain analysis could also be valuable in assessment of the arterial elasticity. In addition, both PPG and EMFi measurements are absolutely non‐invasive and safe. In PPG measurement, the sensors are on the opposite sides of the finger tip, however, EMFi measurement needs the good skilled operator attaching the sensor on the patient’s wrist by touching gently to obtain accurate waveforms