A Printed Circuit Board (PCB) testing method using infrared thermal signatures is presented. The concept of thermal signature for PCBs is introduced. Based on this concept, the testing method is able to classify the integrated circuits (ICs) on a PCB into a number of classes (e.g. functional -fault free, non-functionalfaulty and less reliable -functional circuits with high current consumption). According with thermal signature of each IC on the PCB, the PCBs can be also classified in the same number of classes. The classification system is a feed-forward neural network that learns and classifies the information achieved from the infrared image.
Summary
As the market and the application areas of high capacity battery energy storage systems are rapidly increasing, there is a correspondingly high interest in the topic of minimizing battery state of health degradation in battery packs. In this article, a novel method for battery management in large‐scale battery packs is introduced, aiming to minimize battery degradation by enforcing a special wear leveling (WL) policy, adapted from the flash memory arrays. Using this method in conjunction with a hybrid mathematical‐electrochemical battery model, a reconfigurable battery management system (BMS) is proposed and evaluated. The results of the performance analysis and in‐depth comparisons with other state‐of‐the‐art solution shows that the proposed method achieves significantly longer operating times for the battery packs—for example, 415% improvement over the classical BMS in the load current variation scenario. As the computing and memory requirements are relatively low, the new battery WL method can also be implemented on embedded systems with limited resources.
During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O3 gases and a similar accuracy for the NO2 gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.
The environment sustainability is one of the main directions of intervention for all developed countries. Solutions for alternative energies generation and energy reduction are already discussed. Furthermore, energy efficiency has become an important aspect in data centers and large server systems. Virtualization is one of the main research directions for both large scale data centers and servers. This paper explores how virtualization solutions influence the power consumption of physical systems they are implemented on and which is the most effective way to test and measure the energy efficiency of these solutions. In our tests we used two physical machines (one laptop and one desktop), both running Windows and Linux operating systems and we selected the VMWare virtualization solution.
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