A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries.

We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system's state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods. These methods are presented as a valuable tool for designing life-testing experiments and for maintaining lithium-ion batteries.

Reliability Analysis of Wireless Sensor Network for Smart Farming Applications.

Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network.

Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care.

In this paper, we propose an unobtrusive method and architecture for monitoring a person's presence and collecting his/her health-related parameters simultaneously in a home environment. The system is based on using a single ultra-wideband (UWB) impulse-radar as a sensing device. Using UWB radars, we aim to recognize a person and some preselected movements without camera-type monitoring. Via the experimental work, we have also demonstrated that, by using a UWB signal, it is possible to detect small chest movements remotely to recognize coughing, for example. In addition, based on statistical data analysis, a person's posture in a room can be recognized in a steady situation. In addition, we implemented a machine learning technique (k-nearest neighbour) to automatically classify a static posture using UWB radar data. Skewness, kurtosis and received power are used in posture classification during the postprocessing. The classification accuracy achieved is more than 99%. In this paper, we also present reliability and fault tolerance analyses for three kinds of UWB radar network architectures to point out the weakest item in the installation. This information is highly important in the system's implementation.

Analysis of the Influence of the GPS Errors Occurred While Collecting Electrode Coordinates on the Electrical Resistivity of Tumuli.

In archaeological applications the accurate reconstruction of buried structures is mandatory. Electrical resistivity tomography is widely used for this purpose. Nevertheless, resistivity errors could be generated by wrong placement of electrodes. Papers in the literature do not discuss the influence of errors connected with the electrode position location (GPS-error). In this paper the first results of a Monte Carlo simulation analysis of data acquired on a tumulus are presented. The main research questions were: (i) if it is correct to ignore the GPS-error collect, and (ii) if a minimum threshold, that significantly affect the inversion, exists. Results, obtained considering planimetric GPS-errors of about one third of the fixed electrode distances, show that the GPS-errors affect resistivity, but the generated errors/anomalies: (a) are lower than that obtained without considering the topography, and (b) are significant from a numerical point of view, but do not affect the interpretation, being compatible with the soil resistivity ranges.