Detection Of Sudden Changes Research Articles

Data-driven wind turbine sensor health validation

This paper presents a data-driven approach for detecting anomalies in wind turbine sensors, specifically anemometers and wind vanes, and the development of a smart alerting system. The study focuses on utilizing SCADA and reanalysis (ERA5) data for accurate anomaly detection and reducing false alarms through smart change point detection algorithms. The methodology involves modeling normal behavior, detecting change points, and comparing power curves before and after these points. For anemometer anomaly detection, a three-year SCADA dataset from an offshore wind farm and a synthetic dataset is used, employing an XGBoost model and the PELT algorithm for change point detection. Wind vane anomalies are identified using a nine month dataset from seven turbines, with synthetic alterations to simulate misalignments. Results show successful detection of sudden changes in wind speed and direction, with smart alarms assisting operators in decision-making. This research enhances wind turbine condition monitoring, improving reliability and efficiency.

A self-powered and supercapacitive microneedle continuous glucose monitoring system with a wide range of glucose detection capabilities

Continuous detection of sudden changes in blood glucose is essential for individuals with diabetes who have difficulty in maintaining optimal control of their blood glucose levels. Hypoglycemic shock or a hyperglycemic crisis are likely to occurs in patients with diabetes and poses a significant threat to their lives. Currently, commercial continuous glucose monitoring (CGM) has limits in the glucose concentration detection range, which is 40–500 mg/dL, making it difficult to prevent the risk of hyperglycemic shock. In addition, current CGMs are invasive, cause pain and irritation during usage, and expensive. In this research, we overcome these limitations by introducing a novel mechanism to detect glucose concentration using supercapacitors. The developed CGM, which is self-powered and minimally invasive due to the use of microneedles, can detect a wider range of glucose concentrations than commercial sensors. In addition, efficacy and stability were proven through in vitro and in vivo experiments. Thus, this self-powered, microneedle and supercapacitive-type CGM can potentially prevent both hypoglycemic and complications of hyperglycemia without pain and with less power consumption than current commercial sensors.

How uncertain are you? Disentangling expected and unexpected uncertainty in pupil-linked brain arousal during reversal learning.

During decision making, we are continuously faced with two sources of uncertainty regarding the links between stimuli, our actions, and outcomes. On the one hand, our expectations are often probabilistic, that is, stimuli or actions yield the expected outcome only with a certain probability (expected uncertainty). On the other hand, expectations might become invalid due to sudden, unexpected changes in the environment (unexpected uncertainty). Several lines of research show that pupil-linked brain arousal is a sensitive indirect measure of brain mechanisms underlying uncertainty computations. Thus, we investigated whether it is involved in disentangling these two forms of uncertainty. To this aim, we measured pupil size during a probabilistic reversal learning task. In this task, participants had to figure out which of two response options led to reward with higher probability, whereby sometimes the identity of the more advantageous response option was switched. Expected uncertainty was manipulated by varying the reward probability of the advantageous choice option, whereas the level of unexpected uncertainty was assessed by using a Bayesian computational model estimating change probability and resulting uncertainty. We found that both aspects of unexpected uncertainty influenced pupil responses, confirming that pupil-linked brain arousal is involved in model updating after unexpected changes in the environment. Furthermore, high level of expected uncertainty impeded the detection of sudden changes in the environment, both on physiological and behavioral level. These results emphasize the role of pupil-linked brain arousal and underlying neural structures in handling situations in which the previously established contingencies are no longer valid.

Real-World Application of Early Detection of Sudden Changes in Driver’s Physical Condition Using Machine Learning and Interpretation Method

In order to decrease fatal traffic accidents, early detection of sudden changes in driver’s physical condition is desired. In this paper, we reported an example of modeling process of "driver’s normal behavior model," which is a part of driver state sensing technology we are developing. By combining machine learning and domain knowledge, we have achieved the model for normal steering operation that had both high prediction accuracy and high interpretability/explainability under real-world conditions.

A New Approach to Detection of Changes in Multidimensional Patterns - Part II

Abstract In the paper we develop an algorithm based on the Parzen kernel estimate for detection of sudden changes in 3-dimensional shapes which happen along the edge curves. Such problems commonly arise in various areas of computer vision, e.g., in edge detection, bioinformatics and processing of satellite imagery. In many engineering problems abrupt change detection may help in fault protection e.g. the jump detection in functions describing the static and dynamic properties of the objects in mechanical systems. We developed an algorithm for detecting abrupt changes which is nonparametric in nature and utilizes Parzen regression estimates of multivariate functions and their derivatives. In tests we apply this method, particularly but not exclusively, to the functions of two variables.

Robust control charts for the mean of a locally linear time series

We study residual control charts for the detection of sudden changes in time series with an underlying time-varying trend. Our control charts compare the most recent observations to their direct predecessors by applying two-sample tests in a moving time window to one-step-ahead prediction errors obtained from a local linear regression. Global model assumptions, a fixed target value, or large sets of historical in-control data are unnecessary therefore. Disturbing in-control structures, like short outlier patches or minor fluctuations, are resisted by using robust methods. Our simulation results indicate that a control chart based on the two-sample Hodges-Lehmann estimator in combination with repeated-median regression possesses an approximately distribution-free in-control average run length, good robustness against outliers, and a desirable detection quality.

A Combined MPPT Algorithm for Photovoltaic Systems Based Arduino Microcontroller

Photovoltaic energy is a renewable source of clean and attractive energy compared to conventional energy sources. Photovoltaic panels (PV) have a non-linear I-V characteristic that depends on climatic conditions. In order to operate the photovoltaic modules at their maximum power point (PMM), MPPT algorithms are developing in order to extract the maximum energy. The Perturb and Observe (P & O) technique is widely used in photovoltaic systems because of its simplicity but remains vulnerable to sudden changes in weather conditions. In this paper, a new multi-objective MPPT algorithm combining three techniques: MPPT type (P & O), fractional short-circuits current (FSCC) and fractional open circuit voltage technique (FOCV) is proposed. This multi-objective algorithm improves the performance of conventional MPPT (P & O). A variable pitch perturbation and detection of sudden changes in illumination and / or temperature levels allows better stabilization of the duty cycle once the PPM is reaching. The proposed algorithm is testing by Matlab/Simulink and the results obtained are compared with experimental results using Arduino Uno microcontroller and appropriate sensors.

Photogrammetric methods applied to Svalbard glaciers: accuracies and challenges

Use of digital images is expanding as a tool for glacier monitoring, and smallformat time-lapse cameras are increasingly being used for glacier monitoring of fast-flowing glaciers. Stereoscopic imagery is preferable since it yields direct displacement results but stereo photogrammetry has more requirements regarding geometry in set-up and control points, as well as the additional cost of another complete camera system. We investigate a combination of methods to achieve satisfactory control of accuracy with resulting significant day-to-day velocity variations ranging from 1.5-4 m day-1 made at a distance of 2 km. Validation of results was made by comparing different methods, partly using the same image material, but also in combination with aerial and satellite images. Monoscopic results can also be used to gain continuity in a stereo data set when geometry or visibility is poor. We also explore the use of ordinary photographs taken from airliners for compilation of orthoimages as a potential low cost method for detection of sudden changes. The method, showing some tens of metres accuracy, was verified for monitoring velocities and front positions during a glacier surge and was also used to validate monoscopic timelapse images.Keywords: Photogrammetry; time-lapse; orthoimage; mono- and stereoscopic; surge; velocity(Published: 12 June 2012)Citation: Polar Research 2012, 31, 18671, http://dx.doi.org/10.3402/polar.v31i0.18671

Exploring the sources of ‘missingness’ in brain tissue monitoring datasets: An observational cohort study

Objective: To investigate the frequency/category of missing electronic data in export files from patients admitted to the intensive care unit (ICU) with severe traumatic brain injury (TBI).Research design: Observational cohort study.Methodology: Patient data-streams for brain temperature (Tbr) and intracranial pressure (ICP) were analysed. Missing data was classified as: missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR).Results: Sixty-two patients were studied; 60% of missing Tbr and ICP data events were attributed to electronic data acquisition ‘faults’. Missing data rate ranged from 9–43% (median 25%). Cross-reference of missing data to clinical observation sheets and medical case notes shows that disconnection of sensors from monitors during critical events are common.Conclusions and implications for further research: Of concern for clinical management of patients with TBI is the detection of sudden changes in ICP or brain temperature; the parameters which forewarn of impending intracranial catastrophes. Missing data occurred at critical times of the patients stay in the ICU. This work should alert research clinicians of the need to scrutinize monitored physiological data to establish the percentage of missing values in order to obviate bias in the interpretation of results.

A methodology for combustion detection in diesel engines through in-cylinder pressure derivative signal

In-cylinder pressure measurement has historically been used for off-line combustion diagnosis, but online application for real-time combustion control has become of great interest. This work considers low computing-cost methods for analysing the instant variation of the chamber pressure, directly obtained from the electric signal provided by a traditional piezoelectric sensor. Presented methods are based on the detection of sudden changes in the chamber pressure, which are amplified by the pressure derivative, and which are due to thermodynamic phenomena within the cylinder. Signal analysis tools both in time and in time-frequency domains are used for detecting the start of combustion, the end of combustion and the heat release peak. Results are compared with classical thermodynamic analysis and validated in several turbocharged diesel engines.

Development of a sensor array based measurement system for continuous monitoring of water and wastewater

Non-specific sensor arrays provide a simple, non-invasive technique for monitoring changes in water and wastewater quality. An on-line measurement system that incorporated an array of conducting polymers was tested for continuous analysis both in a laboratory, and in the field. The headspace gas, generated from a sparged liquid sample within a temperature-controlled flow-cell was directly transferred to the sensor chamber for analysis. Experiments were statistically designed to investigate the reproducibility of the sensor responses due to humidity effects. The combined effect and interactions of temperature throughout the system, gas flow rate and sparger porosity were evaluated. An optimal sampling methodology was developed for water and wastewater samples. Selected methodologies were applied to a continuous monitoring system. Results showed high-resolution profiles from laboratory and field trials underlining the potential of the technique for the detection of sudden changes in the odour profile. The interest of appropriate data analysis protocols and of a general quality index for real-time monitoring of water and wastewater is also discussed.

Mismatch responses to randomized gradient switching noise as reflected by fMRI and whole-head magnetoencephalography.

The central auditory system of the human brain uses a variety of mechanisms to analyze auditory scenes, among others, preattentive detection of sudden changes in the sound environment. Electroencephalography (EEG) and magnetoencephalography (MEG) provide a measure to monitor neuronal cortical currents. The mismatch negativity (MMN) or field (MMNm) reflect preattentive activation in response to deviants within a sequence of homogenous auditory stimuli. Functional magnetic resonance imaging (fMRI) allows for a higher spatial resolution as compared to the extracranial electrophysiological techniques. The image encoding gradients of echo planar imaging (EPI) sequences, however, elicit an interfering background noise. To circumvent this shortcoming, the present study applied multi-echo EPI mimicking an auditory oddball design. The gradient trains (SOA = 800 msec, 94.5 dB SPL, stimulus duration = 152 msec) comprised amplitude (-9 dB) and duration (76 msec) deviants in a randomized sequence. Moreover, the scanner noise was recorded and applied in a whole-head MEG device to validate the properties of this specific material. Robust fMRI activation patterns emerged in response to the deviant gradient switching. Changes in amplitude activated the entire auditory cortex, whereas the duration deviants elicited right-lateralized signal increase in secondary areas. The recorded scanner noise evoked reliably right-lateralized mismatch MEG responses. Source localization was in accordance with activation of secondary auditory cortex. The presented paradigm provides a robust and feasible tool to study the functional anatomy of early cognitive auditory processing in clinical populations such as schizophrenia.

A Comprehensive Aerological Reference Data Set (CARDS): Rough and Systematic Errors

The possibility of anthropogenic climate change and the possible problems associated with it are of great interest. However, one cannot study climate change without climate data. The Comprehensive Aerological Reference Data Set (CARDS) project will produce high-quality, daily upper-air data for the research community and for policy makers. CARDS intends to produce a dataset consisting of radiosonde and pibal data that is easy to use, as complete as possible, and as free of errors as possible. An attempt will be made to identify and correct biases in upper-air data whenever possible. This paper presents the progress made to date in achieving this goal. An advanced quality control procedure has been tested and implemented. It is capable of detecting and often correcting errors in geopotential height, temperature, humidity, and wind. This unique quality control method uses simultaneous vertical and horizontal checks of several meteorological variables. It can detect errors that other methods cannot. Research is being supported in the statistical detection of sudden changes in time series data. The resulting statistical technique has detected a known humidity bias in the U.S. data. The methods should detect unknown changes in instrumentation, station location, and data-reduction techniques. Software has been developed that corrects radiosonde temperatures, using a physical model of the temperature sensor and its changing environment. An algorithm for determining cloud coverforthis physical model has been developed. A numerical check for station elevation based on the hydrostatic equations has been developed, which has identified documented and undocumented station moves. Considerable progress has been made toward the development of algorithms to eliminate a known bias in the U.S. humidity data.

Assessing Left Ventricular Filling Pressure with Flow-Directed (Swan-Ganz) Catheters: Detection of Sudden Changes in Patients with Left Ventricular Dysfunction

The accuracy with which pulmonary pressures reflect sudden changes in left ventricular (LV) pressures was studied in 15 patients without mitral disease. Mean pulmonary artery (PA), pulmonary artery end-diastolic (PAd), mean pulmonary capillary (PC), and pulmonary capillary "a" wave (PCa) pressures were compared with simultaneous LV diastolic pressures measured before (LV pre "a") and after atrial systole (LVEDP). Recordings were made at rest and after angiography. There were significant correlations (r greater than 0.75, P less than 0.01) between LV diastolic pressures and each of the pulmonary pressures. The LVEDP and PCa related most closely (r = 0.98). Differences of 5 mm Hg or more between LVEDP and either PAd or PC occurred in more than 50 percent of the observations and were due to large LV "a" waves. The PC, and to a lesser degree the PAd, were more closely related to LV pre "a" than to LVEDP. Changes (delta) in PCa after LV angiography approximated deltaLVEDP, whild deltaPAD and deltaPC reflected deltaLV pre "a" more closely than deltaLVEDP. In conclusion, PCa reflects the LV "a" wave and, thus, is the most accurate index for LVEDP and deltaLVEDP in patients with LV dysfunction. The PC reflects LV pre "a" (mean LV filling pressure) but is an unreliable index of LVEDP. The PC is more easily measured than PCa and, with proper interpretation, should remain the most useful measurement for patient monitoring.