Dynamic Noise Reduction with Deep Residual Shrinkage Networks for Online Fault Classification

Fault signals in high-voltage (HV) power plant assets are captured using the electromagnetic interference (EMI) technique. The extracted EMI signals are taken under different conditions, introducing varying noise levels to the signals. The aim of this work is to address the varying noise levels found in captured EMI fault signals, using a deep-residual-shrinkage-network (DRSN) that implements shrinkage methods with learned thresholds to carry out de-noising for classification, along with a time-frequency signal decomposition method for feature engineering of raw time-series signals. The approach will be to train and validate several alternative DRSN architectures with previously expertly labeled EMI fault signals, with architectures then being tested on previously unseen data, the signals used will firstly be de-noised and a controlled amount of noise will be added to the signals at various levels. DRSN architectures are assessed based on their testing accuracy in the varying controlled noise levels. Results show DRSN architectures using the newly proposed residual-shrinkage-building-unit-2 (RSBU-2) to outperform the residual-shrinkage-building-unit-1 (RSBU-1) architectures in low signal-to-noise ratios. The findings show that implementing thresholding methods in noise environments provides attractive results and their methods prove to work well with real-world EMI fault signals, proving them to be sufficient for real-world EMI fault classification and condition monitoring.

The Importance of Attention in the Twinkle Goes Effect

How do we perceive the location of moving objects? The position and motion literature is currently divided. Predictive accounts of object tracking propose that the position of moving objects is anticipated ahead of sensory signals, whilst non-predictive accounts claim that an anticipatory mechanism is not necessary. A novel illusion called the twinkle goes effect, describing a forward shift in the perceived final location of a moving object in the presence of dynamic noise, presents a novel opportunity to disambiguate these accounts. Across three experiments, we compared the predictions of predictive and non-predictive theories of object tracking by combining the twinkle goes paradigm with a multiple object tracking task. Specifically, we tested whether the size of the twinkle goes illusion would be smaller with greater attentional load (as entailed by the non-predictive, tracking continuation theory) or whether it would not be affected by attentional load (as entailed by predictive extrapolation theory). Our results failed to align with either of these theories of object localisation and tracking. Instead, we found evidence that the twinkle goes effect may be stronger with greater attentional load. We discuss whether this result may be a consequence of an essential, but previously unexplored relationship between the twinkle goes effect and representational momentum. In addition, this study was the first to reveal critical individual differences in the experience of the twinkle goes effect, and in the mislocalisation of moving objects. Together, our results continue to demonstrate the complexity of position and motion perception.

The influence of signal noise to measurements at bridge structure

Bridge weight-in-motion (BWIM) system is a method, that provides to identify axle weights. It is a non-destructive method, which allows not only to identify the axle weight, but it can show current shape of the structure, so it has a great potential. There are various methods to do measurements for this system. Mostly, accelerometers or strain gauge are used. Signal noise has significant effects to the results. It could be resonance of the bridge, wind, defect at the support system, defect at the roadway, etc. It is necessary to filter all this effects, to get clear data. There are many ways to do the filtering. Digital filters allow it. Sometimes, this type of filtering could remove important data about the crossing of the vehicle. It could generate inaccuracy of the whole system and create major errors to identified vehicles. It is necessary to find the optimal way, to keep important data and remove all dynamic noise. This paper will investigate the previously mentioned problems. Measurements will be accomplished on a small-scale model of the bridge. Vehicle will be crossing over the bridge, while the bridge will be awakened to the first vibration shape and other frequencies, that will have a great impact to the measurements.

Response-locked classification image analysis of perceptual decision making in contrast detection

In many situations, humans make decisions based on serially sampled information through the observation of visual stimuli. To quantify the critical information used by the observer in such dynamic decision making, we here applied a classification image (CI) analysis locked to the observer's reaction time (RT) in a simple detection task for a luminance target that gradually appeared in dynamic noise. We found that the response-locked CI shows a spatiotemporally biphasic weighting profile that peaked about 300 ms before the response, but this profile substantially varied depending on RT; positive weights dominated at short RTs and negative weights at long RTs. We show that these diverse results are explained by a simple perceptual decision mechanism that accumulates the output of the perceptual process as modelled by a spatiotemporal contrast detector. We discuss possible applications and the limitations of the response-locked CI analysis.

Features of the use of quartz sand as a vibration damping spacer for internal combustion engine muffler housing

Evaluation of acoustic characteristics of an internal combustion engine (ICE) takes into account its capability to reduce gas dynamic noise from exhausted gases as well as the level of structural (housing) noise emitted by the dynamically excited housing. The muffler housing is dynamically excited in a mechanical way from an ICE vibrating on the suspension as well as by a pulsating flow of exhaust gases transmitting vibration energy via hard support ties of rubber metallic suspension supports on the vehicle frame or body and emitting sound to the surrounding space. One of the methods to reduce the level of muffler housing noise is making the housing double-layered with internal and external walls made of metal, or making it triple-layered containing a vibration damping spacer between the internal and external walls. An efficient solution is the use of a loose vibration damping substances represented by quartz sand as a vibration damping spacer. The article considers the study results of structural sound levels emitted by triple-layered walls of an ICE muffler depending on the bulk density of quartz sand used as a vibration damping spacer between muffler housing walls.

A response-locked classification image analysis of the perceptual decision making : contrast detection

In many situations, humans make decisions based on serially sampled information through the observation of visual stimuli. To quantify the critical information used by the observer in such dynamic decision making, we here applied a classification image (CI) analysis locked to the observer's reaction time (RT) in a simple detection task for a luminance target that gradually appeared in dynamic noise. We found that the response-locked CI shows a spatiotemporally biphasic weighting profile that peaked about 300 ms before the response, but this profile substantially varied depending on RT; positive weights dominated at short RTs and negative weights at long RTs. We show that these diverse results are explained by a simple perceptual decision mechanism that accumulates the output of the perceptual process as modelled by a spatiotemporal contrast detector. We discuss possible applications and the limitations of the response-locked CI analysis.

The Multi-Model Large Ensemble Archive as a climate noise generator: opportunities and outlooks for Observational Large Ensemble construction

<p>The Multi-Model Large Ensemble Archive (MMLEA) is a collection of CMIP5-generation single model initial condition large ensembles (SMILEs) and thus provides estimates of internal variability from several independently developed coupled climate models. Work is underway to determine whether these simulations provide a range of historical regional climate variability suitable for statistically increasing the observed temperature sample.  Alternative sequences of historical temperature can be constructed by combining a forced signal with estimates of internal climate noise; prior studies have used the forced response from one SMILE in concert with observational noise resampling to form an “observational large ensemble” (McKinnon et al. 2018). Analogous to a SMILE, an observational large ensemble can be used to statistically contextualize monthly to half-yearly extreme events, such as the persistently mild Siberian winter of 2020, and to develop additional extended hot or cold spell storylines to explore in future projections of regional climate.</p><p>In this study, an alternative approach to constructing an observational large ensemble of European surface air temperature over the historical period (1950-2014), made possible by the MMLEA, is explored. Rather than relying on forced response and internal variability, components not well-defined in the single realization of observed climate, the constructed circulation analogue method of dynamical adjustment is employed to separate temperature anomalies related to atmospheric circulation (“dynamic noise") from a more thermodynamically driven residual signal. The approach is advantageous because it can be applied in a similar manner to single realizations from both models and observations. Here, dynamic noise is computed by dividing each of the seven CMIP5-generation SMILEs in half and empirically estimating the component of temperature associated with interannual sea level pressure variability in one half of the SMILE using circulation analogues from members in the other half. Because ensemble means can be computed in SMILEs, it is possible to use the relationship between unforced temperature and unforced sea level pressure anomalies to construct dynamic noise. In observations, weekly-averaged analogues are assessed as a means to increase the size of the analogue pool such that the separation between dynamic noise and thermodynamic residual signal occurs in a manner more similar to that computed in the SMILEs.</p><p>The extent to which dynamic noise fields from different SMILEs are distinguishable from each other and from observational estimates is determined via spectral and spatial pattern analyses. To avoid introducing regional model bias into dynamic noise estimates, a mosaic approach will be taken; noise estimates from different models are mosaiced such that observed statistical properties are maintained at each grid point of the European domain. Upon validation, SMILE-derived dynamic noise and observational thermodynamic residual signal estimates are combined into a 50-member European observational large ensemble and evaluated via a multi-month extreme temperature frequency metric against the observational large ensemble developed by McKinnon et al. (2018). Anomalously persistent hot and cold spells found in the European observational large ensemble are further compared to events in out-of-sample future projections of climate from the CMIP6 archive.</p>