Underwater noise recognition of marine vessels passages: two case studies using hidden Markov models

Passive acoustic monitoring (PAM) is emerging as a cost-effective non-intrusive method to monitor the health and biodiversity of marine habitats, including the impacts of anthropogenic noise on marine organisms. When long PAM recordings are to be analysed, automatic recognition and identification processes are invaluable tools to extract the relevant information. We propose a pattern recognition methodology based on hidden Markov models (HMMs) for the detection and recognition of acoustic signals from marine vessels passages and test it in two different regions, the Tagus estuary in Portugal and the Öresund strait in the Baltic Sea. Results show that the combination of HMMs with PAM provides a powerful tool to monitor the presence of marine vessels and discriminate different vessels such as small boats, ferries, and large ships. Improvements to enhance the capability to discriminate different types of small recreational boats are discussed.info:eu-repo/semantics/publishedVersio

Monitoring of Compliance in Western Australian Conservation Contracts

Contracting with private landholders for labor towards production of environmental services (payment for actions) or the environmental services themselves (payment for outcomes) is reliant on the environmental organization’s ability to monitor and assess the environmental outcomes provided. Inaccurate and costly assessment reduces the cost effectiveness of the contract. Different assessment technologies will have different impacts on the cost effectiveness and optimal contracting choice of the environmental organization. The paper compares the influence of field assessment by a local expert, and remote assessment via satellite imagery, on the optimal contracting decision for the Western Australian wheat belt.conservation, environmental, compliance, monitoring, enforcement, environmental regulation, Crop Production/Industries, Environmental Economics and Policy,

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/

Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts

Source apportionment studies use prior exploratory methods that are not purpose-oriented and receptor modelling is based on chemical speciation, requiring costly, time-consuming analyses. Hidden Markov Models (HMMs) are proposed as a routine, exploratory tool to estimate PM10 source contributions. These models were used on annual time series (TS) data from 33 background sites in Spain and Portugal. HMMs enable the creation of groups of PM10 TS observations with similar concentration values, defining the pollutant's regimes of concentration. The results include estimations of source contributions from these regimes, the probability of change among them and their contribution to annual average PM10 concentrations. The annual average Saharan PM10 contribution in the Canary Islands was estimated and compared to other studies. A new procedure for quantifying the wind-blown desert contributions to daily average PM10 concentrations from monitoring sites is proposed. This new procedure seems to correct the net load estimation from deserts achieved with the most frequently used method

Data-driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

Wind power is highly dependent on wind speed and operations offshore are affected by wave height; these together called turbine weather datasets that are variable and intermittent over various time-scales and signify offshore weather conditions. In contrast to onshore wind, offshore wind requires improved forecasting since unfavorable weather prevents repair and maintenance activities. This study proposes two data-driven models for long-term weather conditions forecasting to support operation and maintenance (O&M) decision-making process. These two data-driven approaches are long short-term memory network, abbreviated as LSTM, and Markov chain. An LSTM is an artificial recurrent neural network, capable of learning long-term dependencies within a sequence of data and is typically used to avoid the long-term dependency problem. While, Markov is another data-driven stochastic model, which assumes that, the future states depend only on the current states, not on the events that occurred before. The readily available weather FINO3 datasets are used to train and validate the performance of these models. A performance comparison between these weather forecasted models would be carried out to determine which approach is most accurate and suitable for improving offshore wind turbine availability and support maintenance activities. The entire study outlines the weakness and strength associated with proposed models in relations to offshore wind farms operational activities

HMMoce : an R package for improved geolocation of archival-tagged fishes using a hidden Markov method

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Methods in Ecology and Evolution 9 (2018): 1212-1220, doi:10.1111/2041-210X.12959.Electronic tagging of marine fishes is commonly achieved with archival tags that rely on light levels and sea surface temperatures to retrospectively estimate movements. However, methodological issues associated with light-level geolocation have constrained meaningful inference to species where it is possible to accurately estimate time of sunrise and sunset. Most studies have largely ignored the oceanographic profiles collected by the tag as a potential way to refine light-level geolocation estimates. Open-source oceanographic measurements and outputs from high-resolution models are increasingly available and accessible. Temperature and depth profiles recorded by electronic tags can be integrated with these empirical data and model outputs to construct likelihoods and improve geolocation estimates. The R package HMMoce leverages available tag and oceanographic data to improve position estimates derived from electronic tags using a hidden Markov approach. We illustrate the use of the model and test its performance using example blue and mako shark archival tag data. Model results were validated using independent, known tracks and compared to results from other geolocation approaches. HMMoce exhibited as much as 6-fold improvement in pointwise error as compared to traditional light-level geolocation approaches. The results demonstrated the general applicability of HMMoce to marine animals, particularly those that do not frequent surface waters during crepuscular periods.This work was funded by awards to C. Braun from the Martin Family Society of Fellows for Sustainability Fellowship at the Massachusetts Institute of Technology, the Grassle Fellowship and Ocean Venture Fund at the Woods Hole Oceanographic Institution, and the NASA Earth and Space Science Fellowship

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Monitoring of compliance in Australian conservation contracts

Government and non-government conservation agencies have long-term goals and objectives to provide environmental services, such as conserving the biodiversity of Australian native vegetation. In addition to national parks and reserves, private lands are often included in conservation programs to achieve these objectives. Formal contracts are entered into between the private landholder and the conservation agency to provide environmental services, or more commonly to provide inputs that are likely to lead to environmental services. The paper examines the costs and benefits of monitoring these conservation contracts when biodiversity change is stochastic.conservation, compliance, monitoring, enforcement, environmental regulation, Environmental Economics and Policy,

In situ analysis for intelligent control

We report a pilot study on in situ analysis of backscatter data for intelligent control of a scientific instrument on an Autonomous Underwater Vehicle (AUV) carried out at the Monterey Bay Aquarium Research Institute (MBARI). The objective of the study is to investigate techniques which use machine intelligence to enable event-response scenarios. Specifically we analyse a set of techniques for automated sample acquisition in the water-column using an electro-mechanical "Gulper", designed at MBARI. This is a syringe-like sampling device, carried onboard an AUV. The techniques we use in this study are clustering algorithms, intended to identify the important distinguishing characteristics of bodies of points within a data sample. We demonstrate that the complementary features of two clustering approaches can offer robust identification of interesting features in the water-column, which, in turn, can support automatic event-response control in the use of the Gulper