A weakly supervised active learning framework for non-intrusive load monitoring

Energy efficiency is at a critical point now with rising energy prices and decarbonisation of the residential sector to meet the global NetZero agenda. Non-Intrusive Load Monitoring is a software-based technique to monitor individual appliances inside a building from a single aggregate meter reading and recent approaches are based on supervised deep learning. Such approaches are affected by practical constraints related to labelled data collection, particularly when a pre-trained model is deployed in an unknown target environment and needs to be adapted to the new data domain. In this case, transfer learning is usually adopted and the end-user is directly involved in the labelling process. Unlike previous literature, we propose a combined weakly supervised and active learning approach to reduce the quantity of data to be labelled and the end user effort in providing the labels. We demonstrate the efficacy of our method comparing it to a transfer learning approach based on weak supervision. Our method reduces the quantity of weakly annotated data required by up to 82.6 - 98.5% in four target domains while improving the appliance classification performance

Human in the loop active learning for time-series electrical measurement data

Advanced machine learning algorithms require large datasets, along with good-quality labels to reach state-of-the-art performance. Although measurements themselves can often be easily available, the labelling process is usually a bottleneck. To address this, active learning approaches exploit the fact that different samples provide varying levels of information to the algorithm. However, these approaches often rely on several unrealistic assumptions - an oracle is assumed to provide error-free labels, all at the same cost and effort. We propose novel active learning-based methods for classification of time series measurements, typically obtained from sensors continuously measuring highly fluctuating environmental conditions including electricity consumption, and demonstrate their effectiveness for home energy management applications, where data labelling is a challenge. A new acquisition function is proposed, which accounts for both model and labelling uncertainty and class balancing. A stopping criterion is designed to stop the active learning process after an optimal point is achieved, to reduce labelling effort. We assess the effect of labelling errors on classification performance and propose two ways of mitigating their effects: (i) a re-labelling mechanism based on similarity of provided labels; (ii) a revised loss function based on confidence levels provided by experts. We validate our contributions for energy dissagregation task in a real-world scenario with three application domain experts. Our results show that the proposed methodology significantly improves performance of algorithms transferred to unseen domains with reduced number of labelled samples - from 61% reduction for dishwasher to 93% reduction for kettle

An active learning framework for microseismic event detection

Induced microseismic monitoring has gained increased interest recently, to support various subsurface activities, including geothermal exploration and oil and gas production. To accurately detect and locate origins of microseismisity, deep learning-based methods have become popular due to their high accuracy when trained on large well-labelled datasets. However, though a huge amount of publicly available seismic measurements is available, laballed data to train models is very scarce, since labelling is time consuming and requires very specialist knowledge. Building on our prior work on active learning for time-series data, we propose an active learning method that cleverly picks only a small number of samples to query and stops when the proposed stopping criterion is met. We demonstrate that the proposed approach can save up to 83% of labelling effort even when transferred to a well with different sensing equipment from those used to build the training set

The Effects Of L-Arginine And L-Name On Coronary Flow And Oxidative Stress In Isolated Rat Hearts

The aim of this experimental study was to assess the effects of the acute administration of L-arginine alone and in combination with L-NAME (a non-selective NO synthase inhibitor) on the coronary flow and oxidative stress markers in isolated rat hearts. The experimental study was performed on hearts isolated from Wistar albino rats (n=12, male, 8 weeks old, body mass of 180-200 g). Retrograde perfusion of the isolated preparations was performed using a modified method according to the Langendorff technique with a gradual increase in the perfusion pressure (40–120 cmH2O). The following values were measured in the collected coronary effluents: coronary flow, released nitrites (NO production marker), superoxide anion radical and the index of lipid peroxidation (measured as thiobarbiturate reactive substances). The experimental protocol was performed under controlled conditions, followed by the administration of L-arginine alone (1 mmol) and L-arginine (1 mmol) + L-NAME (30 μmol). The results indicated that L-arginine did not significantly increase the coronary flow or the release of NO, TBARS and the superoxide anion radical. These effects were partially blocked by the joint administration of L-arginine + L-NAME, which indicated their competitive effect. Hence, the results of our study do not demonstrate significant effects of L-arginine administration on the coronary flow and oxidative stress markers in isolated rat hearts

Protective Effects of Galium verum L. Extract against Cardiac Ischemia/Reperfusion Injury in Spontaneously Hypertensive Rats

Galium verum L. (G. verum, lady’s bedstraw) is a perennial herbaceous plant, belonging to the Rubiaceae family. It has been widely used throughout history due to multiple therapeutic properties. However, the effects of this plant species on functional recovery of the heart after ischemia have still not been fully clarified. Therefore, the aim of our study was to examine the effects of methanol extract of G. verum on myocardial ischemia/reperfusion (I/R) injury in spontaneously hypertensive rats (SHR), with a special emphasis on the role of oxidative stress. Rats involved in the research were divided randomly into two groups: control (spontaneously hypertensive rats (SHR)) and G. verum group, including SHR rats treated with the G. verum extract (500 mg/kg body weight per os) for 4 weeks. At the end of the treatment, in vivo cardiac function was assessed by echocardiography. Rats were sacrificed and blood samples were taken for spectrophotometric determination of systemic redox state. Hearts from all rats were isolated and retrogradely perfused according to the Langendorff technique. After a stabilization period, hearts were subjected to 20-minute ischemia, followed by 30-minute reperfusion. Levels of prooxidants were spectrophotometrically measured in coronary venous effluent, while antioxidant enzymes activity was assessed in heart tissue. Cell morphology was evaluated by hematoxylin and eosin (HE) staining. 4-week treatment with G. verum extract alleviated left ventricular hypertrophy and considerably improved in vivo cardiac function. Furthermore, G. verum extract preserved cardiac contractility, systolic function, and coronary vasodilatory response after ischemia. Moreover, it alleviated I/R-induced structural damage of the heart. Additionally, G. verum extract led to a drop in the generation of most of the measured prooxidants, thus mitigating cardiac oxidative damage. Promising potential of G. verum in the present study may be a basis for further researches which would fully clarify the mechanisms through which this plant species triggers cardioprotection

THE EFFECTS OF SULFUR-CONTAINING COMPOUNDS ON REDOX STATUS IN HOMOCYSTEINE-TREATED RATS

There is growing interest in the activity of sulfur-containing compounds on redox balance in physiological and pathological conditions, considering that some of these compounds have not only antioxidative but also pro-oxidative activities. Aim of this study was to assess possible differences in the effects of various sulfur-containing compounds on redox balance of cardiovascular system in its physiological state and in the early onset of hyperhomocysteinemia. This experimental study divided Wistar albino rats into two groups: saline-treated (control) and DL-homocysteine-treated (experimental group). Rats from experimental group were subjected to subchronic subcutaneous administration of DL-homocysteine at dose of 0.45 μmol/g body weight twice a day for 2 weeks. At the end of this period, rats were sacrificed, and blood samples were collected to be analysed for homocysteine concentration and systemic oxidative stress. Isolated rat hearts were excised and attached to the Langendorff apparatus. To assess the effects of acute administration of L-methionine, L-cysteine, N-acetylcysteine, and sodium hydrogen sulfide, the hearts were perfused individually with each of the mentioned substances at same single dose of 0.5 mmol/l for 5 min. In collected samples of coronary venous effluent oxidative stress biomarkers were determined using spectrophotometry. Total homocysteine level was significantly higher in the experimental group than in the control group, and the effects of applied sulfur-containing compounds were significantly different in experimental and control groups. DL-homocysteine induced considerable changes in functioning of cardiovascular system even before an increase in plasma homocysteine values, and action of sulfur-containing compounds varied depending on the presence of homocysteine