Arcing High Impedance Fault Detection Using Real Coded Genetic Algorithm

Safety and reliability are two of the most important aspects of electric power supply systems. Sensitivity and robustness to detect and isolate faults can influence the safety and reliability of such systems. Overcurrent relays are generally used to protect the high voltage feeders in distribution systems. Downed conductors, tree branches touching conductors, and failing insulators often cause high-impedance faults in overhead distribution systems. The levels of currents of these faults are often much smaller than detection thresholds of traditional ground fault detection devices, thus reliable detection of these high impedance faults is a real challenge. With modern signal processing techniques, special hardware and software can be used to significantly improve the reliability of detection of certain types of faults. This paper presents a new method for detecting High Impedance Faults (HIF) in distribution systems using real coded genetic algorithm (RCGA) to analyse the harmonics and phase angles of the fault current signals. The method is used to discriminate HIFs by identifying specific events that happen when a HIF occurs

Investigations Into the Application of Single-Beam Acoustic Backscatter for Describing Shallow Water Marine Habitats

Chapter 1 Producing thematic coral reef benthic habitat maps from single-beam acoustic backscatter has been hindered by uncertainties in interpreting the acoustic energy parameters E1 (~roughness) and E2 (~hardness), typically limiting such maps to sediment classification schemes. In this study acoustic interpretation was guided by high-resolution LIDAR (Light Detection And Ranging) bathymetry. Each acoustic record, acquired from a BioSonics DT-X echosounder and multiplexed 38 and 418 kHz transducers, was paired with a spatially-coincident value of a LIDAR-derived proxy for topographic complexity (Reef-Volume) and its membership to one of eight LIDAR-delineated benthic habitat classes. The discriminatory capabilities of the 38 and 418 kHz signals were generally similar. Individually, the E1 and E2 parameters of both frequencies differentiated between levels of LIDAR Reef-Volume and most benthic habitat classes, but could not unambiguously delineate benthic habitats. Plotted in E1:E2 Cartesian space, both frequencies formed two main groupings: uncolonized sand habitats and colonized reefal habitats. E1 and E2 were significantly correlated at both frequencies; positively over the sand habitats and negatively over the reefal habitats, where the scattering influence of epibenthic biota strengthened the E1:E2 interdependence. However, sufficient independence existed between E1 and E2 to clearly delineate habitats using the multi-echo E1/E2 Bottom Ratio method. The point-by-point calibration provided by the LIDAR data was essential for resolving the uncertainties surrounding the factors informing the acoustic parameters in a large, survey-scale dataset. The findings of this study indicate that properly interpreted single-beam acoustic data can be used to thematically categorize coral reef benthic habitats. Chapter 2 A large-scale acoustic survey was conducted in Apr-May 2008, with the objective of quantifying the abundance and distribution of seasonal drift macroalgae (DMA) in the Indian River Lagoon. Indian River was surveyed from the Sebastian Inlet to its northernmost extent in the Titusville area. Banana River was surveyed from its convergence with the Indian River northward to the Federal Manatee Zone near Cape Canaveral. The survey vessel was navigated along pre-planned lines running east-west and spaced 200 m apart. The river edges were surveyed to a minimum depth of approximately 1.3 m. Hydroacoustic data were collected with a BioSonics DT-X echosounder and two multi-plexed digital transducers operating at 38 and 418 kHz. The 38 and 418 kHz hydroacoustic data were processed with BioSonics Visual Bottom Typer (VBT) seabed classification software to obtain values of E1’ (time integral of the squared amplitude of the 1st part of the 1st echo waveform), E1 (2nd part of 1st echo), E2 (complete 2nd echo), and FD (fractal dimension characterizing the shape of the 1st echo). Following quality analysis, a training dataset was compiled from 131 hydroacoustic + video samples collected across the extent of the study area. The 38 and 418 kHz E1’, E1, E2, and FD datasets were merged and submitted to a series of three discriminant analyses (DA) to refine the training samples into three pure end-member categories; bare substrate, short SAV (typically Caluerpa prolifera, ~10cm or less), and DMA. The Fisher’s linear discriminant functions from the third and final descriptive DA were used to classify each of the 480,000+ hydroacoustic survey records as either bare, short SAV, or DMA. The classified survey records were then used to calculate the biomass of DMA as the product of average DMA cover for a block of ten records times the wet weight of DMA. The DMA biomass was found to be 69,859 metric tons (wet weight) within the 293.1 km2 study area. The acoustically-predicted mean percent cover of DMA was (i) significantly greater within the navigation channels (18.3%) than outside (12.2%), and (ii) significantly greater in the Indian River (12.9%) than in the Banana River (9.3%). The overall predictive accuracy of total SAV (i.e. short SAV plus DMA) was 78.9% (n=246) at three levels of cover (0-33, 33-66, and 66-100%). The Tau coefficient, a measure of the improvement of the classification scheme over random assignment, was 0.683 ± 0.076 (95% CI), i.e. the rate of misclassifications was 68.3% less than would be expected from random assignment of hydroacoustic records to total SAV cover. The incorporation of multi-plexed digital transducers in conjunction with new post-processing techniques realized the goal of establishing an accurate, efficient, and temporally consistent method for acoustically mapping DMA biomass. Chapter 3 This chapter presents the results of a large-scale hydroacoustic survey conducted in April-May 2008. The objective of this study was to map the distribution and vertical extent of muck in the Indian River Lagoon, utilizing the data collected during a seasonal drift macroalgae survey. Indian River was surveyed from the Sebastian Inlet to its northernmost extent in the Titusville area. Banana River was surveyed from its convergence with the Indian River northward to the Federal Manatee Zone near Cape Canaveral. The survey vessel was navigated along pre-planned lines running east-west and spaced 200 m apart, except for when muck was indicated by the oscilloscope display, at which point a meandering path was adopted to demarcate the horizontal extent of muck. Hydroacoustic data were collected with a BioSonics DT-X echosounder and two multi-plexed digital transducers operating at 38 and 420 kHz. The vertical extent of muck was derived from the 38 kHz hydroacoustic signal, which was processed with Visual Analyzer, a fish-finding software package produced by BioSonics Inc. The software was adapted to integrate echo energy below the water-sediment interface, and a set of post-processing algorithms were developed to translate the sub-bottom echo energy profile into continuous scale estimates of muck thickness. In this manner 500,000+ 38 kHz pings were translated into 88,927 geo-located estimates of muck layer thickness, down to a minimum bottom depth of 1 m. Ground-truthing was conducted in July 2008 at twenty sites within the Indian River. The predictions of muck layer thickness were found to be accurate over the ground-truthed range of 0-3m (r2 = 0.882, SE=0.52m). The vertical distribution of acoustically-predicted muck demonstrated the tendency for muck to accumulate in deeper areas of the lagoon. For the case of Indian River (excluding navigation channels), muck was not detected in depths shallower than 1.4m and rare in the range of 1.4-2.2 m (only 3.6% of records had a predicted muck thickness greater than 0.5 m). The frequency of muck plateaued between 2.2-3.4 m (9.6%) before making a sharp rise to 82% in the range of 4-5 m. As expected, the mean muck layer thickness was significantly greater within the navigation channels (0.56 m) than outside of them (0.08 m). A significant latitudinal trend of muck thickness was detected within the Indian River navigation channels. The mean muck thickness decreased from 1.38 m at its northernmost origins to 0.83 m in the Titusville area before plateauing at approximately 0.4 m for the remainder of segments. Outside of the main ICW channels, 23 individual muck deposits were identified; 22 in the Indian River and 1 in the Banana River. Factors in descending order of co-occurrence were proximity to causeways or jetties, riverbed depressions, and proximity to shore and drainage channels. In conclusion, this study establishes that a single-beam acoustic survey is a cost-effective and accurate alternative for mapping the distribution and vertical extent of muck deposits in the shallow-water environment of the Indian River Lagoon. Moreover, the temporal consistency afforded by a digital transducer allows for direct and meaningful comparisons between successive surveys. Chapter 4 A thematic map of benthic habitat was produced for a coral reef in the Republic of Palau, utilizing hydroacoustic data acquired with a BioSonics DT-X echosounder and a single-beam 418 kHz digital transducer. This paper describes and assesses a supervised classification scheme that used a series of three discriminant analyses (DA) to refine training samples into end-member structural and biological elements, utilizing E1′ (leading edge of 1st echo), E1 (trailing edge of 1st echo), E2 (complete 2nd echo), fractal dimension (1st echo shape), and depth as predictor variables. Hydroacoustic training samples were assigned to one of six predefined groups based on the plurality of benthic elements (sand, sparse SAV, rubble, pavement, rugose hardbottom, branching coral), visually estimated from spatially co-located ground-truthing videos. Records that classified incorrectly or failed to exceed a minimum probability of group membership were removed from the training dataset until only ‘pure’ end-member records remained. This refinement of ‘mixed’ training samples circumvented the dilemma typically imposed by the benthic heterogeneity of coral reefs, i.e. to either train the acoustic ground discrimination system (AGDS) on homogeneous benthos and leave the heterogeneous benthos un-classified, or attempt to capture the many ‘mixed’ classes and overwhelm the discriminatory capability of the AGDS. This was made possible by a conjunction of narrow beamwidth (6.4o) and shallow depth (1.2 to 17.5 m), which produced a sonar footprint small enough to resolve most of the microscale features used to define benthic groups. Survey data classified from the 3rd-Pass training DA were found to (i) conform to visually-apparent contours of satellite imagery, (ii) agree with the structural and biological delineations of a benthic habitat map created from visual interpretation of 2004 IKONOS imagery, and (iii) yield values of benthic cover that agreed closely with independent, contemporaneous video transects. The methodology was proven on a coral reef environment for which high quality satellite imagery existed, as an example of the potential for single-beam systems to thematically map coral reefs in deep or turbid settings where optical methods are unsatisfactory. Chapter 5 Beginning In the winter of 2003-2004, several episodes of red drift macroalgae blooms resulted in massive amounts of macroalgae washing ashore the beaches of Sanibel Island, Bonita Springs, and Ft Meyer Florida. A study conducted after the first event supported a link to increasing land-based nutrient enrichment. A large-scale program was initiated in May 2008, with the primary goal of further defining the possible roles and sources of nutrient enrichment with respect to nuisance macroalgae blooms. This study reports the results of the hydroacoustic mapping component of this program. The goal of this study was to identify areas of substrate suitable for supporting a macroalgae bloom. Areas within San Carlos Bay and offshore Sanibel Island, FL were hydroacoustically surveyed from nearshore to about 11 km offshore during the periods of October 6-10, 2008 and May 10-22, 2009. The hydroacoustic data was acquired with a BioSonics DT-X echosounder and a multiplexed single-beam digital transducers operating at 38 and 418 kHz. Eleven acoustic parameters derived from the 38 and 418 kHz signals were utilized to classify the survey data into 5 ascending categories of visually-apparent seabed roughness. Classes 1 and 2 were both primarily constituted of unconsolidated silt and sand-sized sediments, unsuitable for a bloom. Class 3 is a marginal substrate for a bloom, consisting of packed sand and large intact shell debris. Classes 4 and 5 offer the best attachment sites for a bloom, consisting of consolidated shell hash, live hardbottom, and submerged aquatic vegetation. The majority (~ 80%) of acoustic classifications were of soft bottom sediments (classes 1-2), but there were two significant expanses of rough seabed suitable for macroalgae attachment. These two areas covered a total of 19 km2, within which ~ 56% of the hydroacoustic records classified as “rough” (classes 3-5). The first was a large area of seagrass beds and live hardbottom in the mouth of San Carlos Bay, where large amounts of macroalgae were variably present during the April-May 2009 surveys. The second was offshore Lighthouse Point, near the mouth of San Carlos Bay, situated near a large sand spit that extended from the beach to approximately 6 km offshore. Along the west side of the sand spit there were substantial areas of moderate to high bottom roughness, mostly in the form of consolidated shell hash. The average depths of these two acoustically-rough areas were only 5.0 and 4.0 m, so sufficient irradiance to initiate a bloom could be assumed. These textured and shallow areas on or near the mouth of San Carlos Bay are presumably potential sources for macroalgae attachment and growth, which could easily be transported onto the beaches under some storm conditions given the close proximity to the shoreline. In contrast, the areas in open Gulf of Mexico waters were classified predominantly as soft sediments with low bottom roughness. The site offshore Redfish Pass had a moderate (~22%) proportion of “rough” classifications out to 5km offshore, but from 5-10km offshore the bottom classified as \u3e95% soft sediments. The other two Gulf of Mexico sites classified as \u3e95% soft sediments from nearshore to 11 km offshore. Independent, concurrent video transects indicated there were small areas with large amounts of shell and live hard bottom that occurred sporadically greater than 10km offshore, but all things considered the open Gulf waters around Sanibel Island may not be a major source of drift macroalgae. Chapter 6 This study presents the results of methods developed for acoustic remote sensing of Acropora cervicornis, a threatened species of scleractinian sporadically occurring on the nearshore hardbottom of Southeast Florida. The objective was to develop techniques for mapping isolated Acropora patches on a scale larger than what is feasible using on-the-ground methods. A time-series of A. cervicornis cover could inform resource managers about the fate of such patches, e.g. do they appear and vanish, creep by extension from a central point, or leap by colony fragmentation. The main challenge to acoustically mapping A. cervicornis was distinguishing it from gorgonians occupying the same habitat. Hydroacoustic surveys were conducted in October 2009 at two nearshore sites in Broward County, FL utilizing a BioSonics DT-X echosounder and multiplexed single-beam digital transducers operating at frequencies of 38 and 418 kHz. NCRI scientists have monitored the spatial extent and percent cover of A. cervicornis within these sites, providing an ideal background against which to calibrate the hydroacoustic predictions. Two approaches were evaluated. The first approach utilized BioSonics EcoSAV post-processing software, designed to predict areal cover and canopy height of submerged aquatic vegetation using a series of heuristic pattern-recognition algorithms. Anchored over A. cervicornis, the 38 and 418 kHz signals performed similarly well. Anchored over gorgonians, the 38 kHz signal detected the canopy roughly half as frequently as the 418 kHz signal. Undifferentiated 418 kHz EcoSAV cover was allocated to either A. cervicornis or gorgonians exploiting this frequency-dependent detection. The second approach utilized the acoustic energy (E0, E1′, E1, and E2) and shape (fractal dimension) parameters obtained from BioSonics Visual Bottom Typer software. A dual-frequency training dataset was used to classify records as sand, bare pavement, gorgonians, or A. cervicornis. Both approaches yielded promising results, based on a number of metrics, unambiguously demonstrating that single-beam AGDS are capable of reliably detecting A. cervicornis and gorgonians under controlled conditions

AI Applications to Power Systems

Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

Smart FRP Composite Sandwich Bridge Decks in Cold Regions

INE/AUTC 12.0

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Recognition and assessment of seafloor vegetation using a single beam echosounder

This study focuses on the potential of using a single beam echosounder as a tool for recognition and assessment of seafloor vegetation. Seafloor vegetation is plant benthos and occupies a large portion of the shallow coastal bottoms. It plays a key role in maintaining the ecological balance by influencing the marine and terrestrial worlds through interactions with its surrounding environment. Understanding of its existence on the seafloor is essential for environmental managers.Due to the important role of seafloor vegetation to the environment, a detailed investigation of acoustic methods that can provide effective recognition and assessment of the seafloor vegetation by using available sonar systems is necessary. One of the frequently adopted approaches to the understanding of ocean environment is through the mapping of the seafloor. Available acoustic techniques vary in kinds and are used for different purposes. Because of the wide scope of available techniques and methods which can be employed in the field, this study has limited itself to sonar techniques of normal incidence configuration relative to seafloors in selected regions and for particular marine habitats. For this study, a single beam echosounder operating at two frequencies was employed. Integrated with the echosounder was a synchronized optical system. The synchronization mechanism between the acoustic and optical systems provided capabilities to have very accurate groundtruth recordings for the acoustic data, which were then utilized as a supervised training data set for the recognition of seafloor vegetation.In this study, results acquired and conclusions made were all based on the comparison against the photographic recordings. The conclusion drawn from this investigation is only as accurate as within the selected habitat types and within very shallow water regions.In order to complete this study, detailed studies of literature and deliberately designed field experiments were carried out. Acoustic data classified with the help of the synchronized optical system were investigated by several methods. Conventional methods such as statistics and multivariate analyses were examined. Conventional methods for the recognition of the collected data gave some useful results but were found to have limited capabilities. When seeking for more robust methods, an alternative approach, Genetic Programming (GP), was tested on the same data set for comparison. Ultimately, the investigation aims to understand potential methods which can be effective in differentiating the acoustic backscatter signals of the habitats observed and subsequently distinguishing between the habitats involved in this study