Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Comparative immunogenicity of M. hyopneumoniae NrdF encoded in different expression systems delivered orally via attenuated S. typhimurium aroA in mice.

The Mycoplasma hyopneumoniae ribonucleotide reductase R2 subunit (NrdF) gene fragment was cloned into eukaryotic and prokaryotic expression vectors and its immunogenicity evaluated in mice immunized orally with attenuated Salmonella typhimurium aroA CS332 harboring either of the recombinant expression plasmids. We found that NrdF is highly conserved among M. hyopneumoniae strains. The immunogenicity of NrdF was examined by analyzing antibody responses in sera and lung washes, and the cell-mediated immune (CMI) response was assessed by determining the INF-[gamma] level produced by splenocytes upon in vitro stimulation with NrdF antigen. S. typhimurium expressing NrdF encoded by the prokaryotic expression plasmid (pTrcNrdF) failed to elicit an NrdF-specific serum or secretory antibody response, and IFN-[gamma] was not produced. Similarly, S. typhimurium carrying the eukaryotic recombinant plasmid encoding NrdF (pcNrdF) did not induce a serum or secretory antibody response, but did elicit significant NrdF-specific IFN-[gamma] production, indicating induction of a CMI response. However, analysis of immune responses against the live vector S. typhimurium aroA CS332 showed a serum IgG response but no mucosal IgA response in spite of its efficient invasiveness in vitro. In the present study we show that the DNA vaccine encoding the M. hyopneumoniae antigen delivered orally via a live attenuated S. typhimurium aroA can induce a cell-mediated immune response. We also indicate that different live bacterial vaccine carriers may have an influence on the type of the immune response induced.

Infection-induced IL-10 and Jak-Stat: A review of the molecular circuitry controlling immune hyperactivity in response to pathogenic microbes

Generation of effective immune responses against pathogenic microbes depends on a fine balance between pro- and anti-inflammatory responses. Interleukin-10 (IL-10) is essential in regulating this balance and has garnered renewed interest recently as a modulator of the response to infection at the JAK-STAT signaling axis of host responses. Here, we examine how IL-10 functions as the “master regulator” of immune responses through JAK-STAT, and provide a perspective from recent insights on bacterial, protozoan, and viral infection model systems. Pattern recognition and subsequent molecular events that drive activation of IL-10-associated JAK-STAT circuitry are reviewed and the implications for microbial pathogenesis are discussed.

Effects of secondary crops on bacterial growth and nitrogen removal in shrimp farm effluent treatment systems

Secondary crops provide a means of assimilating some effluent nitrogen from eutrophic shrimp farm settlement ponds. However, a more important role may be their stimulation of beneficial bacterial nitrogen removal processes. In this study, bacterial biomass, growth and nitrogen removal capacity were quantified in shrimp farm effluent treatment systems containing vertical artificial substrates and either the banana shrimp Penaeus merguiensis (de Man) or the grey mullet, Mugil cephalus L. Banana shrimp were found to actively graze biofilm on the artificial substrates and significantly reduced bacterial biomass relative to a control (24.5 ± 5.6mgCm−2 and 39.2 ± 8.7mgCm−2, respectively). Bacterial volumetric growth rates, however, were significantly increased in the presence of the shrimp relative to the control 45.2±11.3mgCm−2 per day and 22.0±4.3mgCm−2 per day, respectively). Specific growth rate, or growth rate per cell, of bacteria was therefore appreciably stimulated by the banana shrimp. Nitrate assimilation was found to be significantly higher on grazed substrate biofilm relative to the control (223±54 mgNm−2 per day and 126±36 mg Nm−2 per day, respectively), suggesting that increased bacterial growth rate does relate to enhanced nitrogen uptake. Regulated banana shrimp feeding activity therefore can increase the rate of newbacterial biomass production and also the capacity for bacterial effluent nitrogen assimilation. Mullet had a negligible influence on the biofilm associated with the artificial substrate but reduced sediment bacterial biomass (224 ± 92 mgCm−2) relative to undisturbed sediment (650 ± 254 mgCm−2). Net, or volumetric bacterial growth in the sediment was similar in treatments with and without mullet, suggesting that the growth rate per cell of bacteria in grazed sediments was enhanced. Similar rates of dissolved nitrogen mineralisation werefound in sediments with and without mullet but nitrificationwas reduced. Presence of mullet increased water column suspended solids concentrations, water column bacterial growth and dissolved nutrient uptake. This study has shown that secondary crops, particularly banana shrimp, can play a stimulatory role in the bacterial processing of effluent nitrogen in eutrophic shrimp effluent treatment systems.

Treatment of shrimp farm effluent with omnivorous finfish and artificial substrates

Long-term environmental sustainability and community acceptance of the shrimp farming industry in Australia requires on-going development of efficient cost-effective effluent treatment options. In this study, we aimed to evaluate the effectiveness of a shrimp farm treatment system containing finfish and vertical artificial substrates (VAS). This was achieved by (1) quantifying the individual and collective effects of grey mullet (Mugil cephalus L.) and VASs on water and sediment quality, and (2) comparing the retention of N in treatment systems with and without the presence of finfish (M. cephalus and the siganid Siganus nebulosus (Quoy & Gaimard)), where light was selectively removed. Artificial substrates were found to significantly improve the settlement of particulate material, regardless of the presence of finfish. Mullet actively resuspended settled solids and reduced the production of nitrate when artificial substrates were absent. However, appreciable nitrification was observed when mullet were present together with artificial substrates. The total quantity of N retained by the mullet was found to be 1.8– 2.4% of the incoming pond effluent N. It was estimated that only 21% of the pond effluent N was available for mullet consumption. When S. nebulosus was added, total finfish N retention increased from 1.8% to 3.9%, N retention by mullet also improved (78±16 to 132±21-mg N day−1 before and after siganid addition respectively). Presence of filamentous macroalgae (Enteromorpha spp.) was found to improve the removal of N from pond effluent relative to treatments where light was excluded. Denitrification was also a significant sink for N (up to 24% N removed). Despite the absence of algal productivity and greater availability of nitrate, denitrification was not higher in treatments where light was excluded. Mullet were found to have no effect on the rates of denitrification but significantly reduced macroalgal growth on the surface of the water. When mullet were absent, excessive macroalgal growth led to reduced dissolved oxygen concentrations and nitrification. This study concludes that the culture of mullet alone in shrimp farm effluent treatment systems does not result in significant retention of N but can contribute to the control of macroalgal biomass. To improve N retention and removal, further work should focus on polyculturing a range of species and also on improving denitrification.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.

Comparison of ANN models for predicting water quality in distribution systems

Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and ``noisy'' data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.

Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.

Artificial commutation for inversion into a weak AC system in a multiterminal HVDC system

This paper deals with the application of artificial commutation for a normally rated inverter connecting a weak AC system in a multiterminal HVDC (MTDC) system. Artificial commutation is achieved using series capacitors. A modular digital simulation technique is developed to study the dynamic performance of the system. It is shown that by a proper selection of the value of the capacitor it is possible to limit the valve stresses and the DC harmonics to acceptable levels and achieve an improved performance during severe transient conditions. The determination of the value of the series capacitor is based on a parametric study.

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.