This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F${}_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e. data with sampling rates lower than the AC base frequency. We first review the many degrees of freedom of these approaches, what has already been done in literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported MAE and F$_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10\,s, a large field of view, the usage of GAN losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

Introduction: There have been several cases reporting a significant increase in chondroitin sulphate plasma levels in patients with different types of disease, such as systemic lupus erythematosus, rheumatoid arthritis, and liver disease. At present, the precise role of chondroitin sulphate molecules in blood is unclear. Previous investigations have shown that the addition of purified human plasma glycosaminoglycans (GAGs), containing a high percentage of chondroitin-4-sulphate (C4S) was able to inhibit lipid peroxidation and to protect cells from reactive oxygen species damage, suggesting antioxidant activity. Starting from these reports, the aim of this study was to evaluate the effectiveness of GAG structures purified from normal human plasma in reducing inflammation using a model of lipopolysaccharide (LPS)-induced increase of pro-inflammatory cytokines in mouse articular chondrocyte cultures.

Grid-connected photovoltaic (PV) capacity is increasing and is currently estimated to account for 3.0% of worldwide energy generation. One strategy to balance fluctuating PV power is to incentivize self-consumption by shifting certain loads. The potential improvement in the amount of self-consumption is usually estimated using smart meter and PV production data. Smart meter data are usually available only at sampling frequences far below the Nyquist limit. In this paper we investigate how this insufficient sampling rate affects the estimated self-consumption potential of shiftable household appliances (washing machines, tumble dryers and dishwashers). We base our analyses on measured consumption data from 16 households in the UK and corresponding PV data. We found that the simulated results have a marked dependence on the data sampling rate. The amount of self-consumed energy estimated with data sampled every 10 min was overestimated by 30–40% compared to estimations using data with 1 min sampling rate. We therefore recommend to take this factor into account when making predictions on the impact of appliance load shifting on the rate of self-consumption.

We present a novel technology for tactile haptic feedback tailored to Virtual Reality (VR) and Augmented Reality (AR) experiences. The aim of this work was to develop and evaluate a novel technology which we first evaluate for its applicability for tactile haptic feedback (THF); in a later step we aim to build a glove-based system combining tactile and kinesthetic haptic feedback (KHF) based on the same technology. Unlike piezoelectric benders, eccentric rotating masses (ERM), and linear resonant actuators (LRA) we present an application of piezoelectric motors aimed at providing realistic tactile haptic feedback. In this research demo, we explain the context of application, the principle of operation, specifics to the implementation, and a technology evaluation. In the last section, we will sketch possible next iterations for an even more immersive haptic experience.