Early Warning Detection System Research Articles

A search method for a hypersonic gliding vehicle based on early warning information guidance

The high speed and high manoeuvrability of a hypersonic gliding vehicle (HGV) pose a severe challenge to the existing early warning detection system. Optimising the search resources of a phased array radar to improve the HGV detecting efficiency has become a practical problem. In this study, a HGV search method based on a hybrid optimisation algorithm is proposed with the early warning information guidance as a priori condition. Firstly, the HGV priority judgement model is established, and the priority quantification equations for height, velocity and distance are designed. Secondly, the search parameters of the radar are optimised with the maximum cumulative detection probability, the shortest average discovery time and the highest priority level as the objective functions, and a radar search model is established. Finally, to overcome the problem that particle swarm optimisation (PSO) is easy to fall into local optimal solution, a hybrid optimisation algorithm based on differential evolution (DE) and PSO is proposed. The performance of the proposed method is verified in two simulation scenarios, and the results show that the proposed method outperforms existing mainstream search methods and can reasonably allocate search resources according to the priority of HGV.

Investigating optimal unmanned aircraft systems flight plans for the detection of marine ingress

From the shutting down of coastal tourism industries, the mass destruction of aquaculture, to the clogging of power station water intakes, marine ingress events have the potential to cause widespread disruption along our coastlines. To gain the ability to respond to such events, efforts are being made to advance the understanding of bloom events which predominantly present as large aggregations of jellyfish, or detached aquatic macroalgaes in the water column. This paper investigates the optimal flight search patterns with a focus on marine ingress bloom detection from unmanned aircraft systems (UAS). The detection performance of four flight search patterns are examined against five different bloom shapes. Monte-Carlo simulations are deployed to assess probable performance of flight search pattern against variable bloom shapes. A total of 50,000 simulated flights were conducted, offering a maximum of 500 million marine ingress objects for possible detection. A two phased flight approach is proposed, with first phase flights conducted as area search strategies, and second phase flights as datum searches for scenarios where some information of possible bloom location is available. Parallel sweep was found to be the best performing generalist flight search pattern, closely followed by the phase two search pattern expanding square. Crossing barrier was found to be competitive but appeared to lend itself towards specific detection scenarios with sector search being a consistently poor performing flight search pattern. This paper also investigates the comparative performance of visual line of sight (VLOS), extended visual line of sight (EVLOS), and beyond visual line of sight (BVLOS) operations. Increase of total survey area was found to increase bloom detection frequency, with BVLOS operations the highest performer successfully increasing bloom detection by a factor of 3.7. This paper exhibits the first assessment of flight search patterns within the context of drone-based detection of marine ingress bloom events. This should facilitate the development of an early warning detection system that can provide reliable warning to coastal industries prior to a marine ingress event occurring.

Quantifying Spatial Drought Propagation Potential in North America Using Complex Network Theory

AbstractDroughts have a dominant three‐dimensional (3‐D) spatiotemporal structure typically spanning hundreds of kilometers and often lasting for months to years. Here, we introduced a novel framework to explore the 3‐D structure of the evolution of droughts based on network theory concepts. The proposed framework is applied to identify critical source regions responsible for large‐scale drought onsets during 1901–2014 for the North American continent using the Standardized Precipitation Evaporation Index (SPEI). We built a spatial network connecting the drought onset timings for the North American continent. Using a spatially weighted network partitioning algorithm, the whole continent is then classified into regional spatial drought networks (RSN), where droughts are more likely to propagate within these regional systems. Finally, a customized network metric was applied to identify locations (source regions) where the drought onsets further propagate to other areas within the regional spatial network. Our results indicated that the West coast, Texas coastal region, and Southeastern Arkansas as major source regions through which atmospheric drought propagates to Western, South Central, and Eastern North America. The formation of drought source regions are due to presence of high pressure ridges and anomalous wind patterns. Furthermore, our results indicate that the drought propagation from these source regions may be due to inadequate moisture transport. The proposed framework can help to develop an early warning detection system for droughts and other spatially extensive extreme events such as heatwaves and floods.

Multidimensional Ground Reaction Forces and Moments From Wearable Sensor Accelerations via Deep Learning.

Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near real-time and during match play. However, current methods are constrained to laboratory instrumentation, are labor and cost intensive, and require highly trained specialist knowledge, thereby limiting their ecological validity and wider deployment. An informative next step towards this goal would be a new method to obtain ground kinetics in the field. Here we show that kinematic data obtained from wearable sensor accelerometers, in lieu of embedded force platforms, can leverage recent supervised learning techniques to predict near real-time multidimensional ground reaction forces and moments (GRF/M). Competing convolutional neural network (CNN) deep learning models were trained using laboratory-derived stance phase GRF/M data and simulated sensor accelerations for running and sidestepping maneuvers derived from nearly half a million legacy motion trials. Then, predictions were made from each model driven by five sensor accelerations recorded during independent inter-laboratory data capture sessions. The proposed deep learning workbench achieved correlations to ground truth, by maximum discrete GRF component, of vertical Fz 0.97, anterior Fy 0.96 (both running), and lateral Fx 0.87 (sidestepping), with the strongest mean recorded across GRF components 0.89, and for GRM 0.65 (both sidestepping). These best-case correlations indicate the plausibility of the approach although the range of results was disappointing. The goal to accurately estimate near real-time on-field GRF/M will be improved by the lessons learned in this study. Coaching, medical, and allied health staff could ultimately use this technology to monitor a range of joint loading indicators during game play, with the aim to minimize the occurrence of non-contact injuries in elite and community-level sports.

Design of Inshore Water Monitoring System Based on IOT

In recent years, countries around the world have deployed a large number of advanced unmanned submersibles in the deep sea to build a complete underwater surveillance system. For the effective detection of threat from underwater, underwater should build three-dimensional multi-dimensional early warning detection system, the development potential of new communication technologies right. This paper designed an offshore environmental monitoring systems. The system includes two subsystems, which waters the information detection subsystem enables real-time monitoring of water information, data distribution subsystem monitoring data can be sent to the specified users through the default network.The system is deployed to the port and nearby offshore waters can achieve anti-submarine reconnaissance information network set up, the next step with advanced acoustic detector can play an important role in the anti-war underwater unmanned underwater vehicles..

Using 1st Derivative Reflectance Signatures within a Remote Sensing Framework to Identify Macroalgae in Marine Environments

Macroalgae blooms (MABs) are a global natural hazard that are likely to increase in occurrence with climate change and increased agricultural runoff. MABs can cause major issues for indigenous species, fish farms, nuclear power stations, and tourism activities. This project focuses on the impacts of MABs on the operations of a British nuclear power station. However, the outputs and findings are also of relevance to other coastal operators with similar problems. Through the provision of an early-warning detection system for MABs, it should be possible to minimize the damaging effects and possibly avoid them altogether. Current methods based on satellite imagery cannot be used to detect low-density mobile vegetation at various water depths. This work is the first step towards providing a system that can warn a coastal operator 6–8 h prior to a marine ingress event. A fundamental component of such a warning system is the spectral reflectance properties of the problematic macroalgae species. This is necessary to optimize the detection capability for the problematic macroalgae in the marine environment. We measured the reflectance signatures of eight species of macroalgae that we sampled in the vicinity of the power station. Only wavelengths below 900 nm (700 nm for similarity percentage (SIMPER)) were analyzed, building on current methodologies. We then derived 1st derivative spectra of these eight sampled species. A multifaceted univariate and multivariate approach was used to visualize the spectral reflectance, and an analysis of similarities (ANOSIM) provided a species-level discrimination rate of 85% for all possible pairwise comparisons. A SIMPER analysis was used to detect wavebands that consistently contributed to the simultaneous discrimination of all eight sampled macroalgae species to both a group level (535–570 nm), and to a species level (570–590 nm). Sampling locations were confirmed using a fixed-wing unmanned aerial vehicle (UAV), with the collected imagery being used to produce a single orthographic image via standard photogrammetric processes. The waveband found to contribute consistently to group-level discrimination has previously been found to be associated with photosynthetic pigmentation, whereas the species-level discriminatory waveband did not share this association. This suggests that the photosynthetic pigments were not spectrally diverse enough to successfully distinguish all eight species. We suggest that future work should investigate a Charge-Coupled Device (CCD)-based sensor using the wavebands highlighted above. This should facilitate the development of a regional-scale early-warning MAB detection system using UAVs, and help inform optimum sensor filter selection.

Inherently safer reactors and procedures to prevent reaction runaway

Abstract Reaction runaway has longtime been an issue in chemical industry as it often leads to severe accidents if not controlled and inhibited properly. Herein we have reviewed several key considerations and procedures to prevent such phenomena, including inherently safer reactor design, thermal risk assessment and early warning detection of runaway, and pointed out that the basic principle underlying is necessary heat management and construction of resilient processes. For inherently safer reactor design, important factors such as heat removal, heat capacitance, flow behaviors and explosive behaviors have been investigated. The survey shows that heat exchanger (HEX) reactor and microreactor outperform traditional reactors. Meanwhile, we have looked into the effect of thermal risk ranking and safety operation region determining for thermal risk assessment, and the influence of runaway criteria and construction methods for early detection of reaction runaway as well. It shows that thermal risk assessment plays a key role on process design, and early warning detection system (EWDS) is preferable on prevention of reaction runaway. In the end, perspectives regarding inherently safer designs with the measures discussed above have been provided.

Improving patient safety during introduction of novel medical devices through cumulative summation analysis.

OBJECTIVE The aim of this study was to implement cumulative summation (CUSUM) analysis as an early-warning detection and quality assurance system for preclinical testing of the iSYS1 novel robotic trajectory guidance system. METHODS Anatomically accurate 3D-printed skull phantoms were created for 3 patients who underwent implantation of 21 stereoelectroencephalography electrodes by surgeons using the current standard of care (frameless technique). Implantation schema were recreated using the iSYS1 system, and paired accuracy measures were compared with the previous frameless implantations. Entry point, target point, and implantation angle accuracy were measured on postimplantation CT scans. CUSUM analysis was undertaken prospectively. RESULTS The iSYS1 trajectory guidance system significantly improved electrode entry point accuracies from 1.90 ± 0.96 mm (mean ± SD) to 0.76 ± 0.57 mm (mean ± SD) without increasing implantation risk. CUSUM analysis was successful as a continuous measure of surgical performance and acted as an early-warning detection system. The surgical learning curve, although minimal, showed improvement after insertion of the eighth electrode. CONCLUSIONS The iSYS1 trajectory guidance system did not show any increased risk during phantom preclinical testing when used by neurosurgeons who had no experience with its use. CUSUM analysis is a simple technique that can be applied to all stages of the IDEAL (idea, development, exploration, assessment) framework as an extra patient safety mechanism. Further clinical trials are required to prove the efficacy of the device.

Design and Implementation of a Coastal-Mounted Sensor for Oil Film Detection on Seawater.

The routine surveillance of oil spills in major ports is important. However, existing techniques and sensors are unable to trace oil and micron-thin oil films on the surface of seawater. Therefore, we designed and studied a coastal-mounted sensor, using ultraviolet-induced fluorescence and fluorescence-filter systems (FFSs), to monitor oil spills and overcome the disadvantages of traditional surveillance systems. Using seawater from the port of Lingshui (Yellow Sea, China) and six oil samples of different types, we found that diesel oil’s relative fluorescence intensity (RFI) was significantly higher than those of heavy fuel and crude oils in the 180–300 nm range—in the 300–400 nm range, the RFI value of diesel is far lower. The heavy fuel and crude oils exhibited an opposite trend in their fluorescence spectra. A photomultiplier tube, employed as the fluorescence detection unit, efficiently monitored different oils on seawater in field experiments. On-site tests indicated that this sensor system could be used as a coastal-mounted early-warning detection system for oil spills.

Coupling a bio-accumulator organism and MALDI-TOF MS: an early warning detection system for microcystins in water bodies

Microcystins produced by freshwater cyanobacteria pose serious threats to human health and are a growing problem in drinking water supplies worldwide. Toxin detection and identification in water reservoirs when cyanobacterial density is still low is a key step to predict harmful algal blooms and to allow the safe use of the resource. For this purpose, developing sensitive and rapid methods of analysis is crucial. Adopting a strategy that couples the use of a filter-feeder organism (Unio elongatulus, Pfeiffer 1825 (Bivalvia: Unionidae)) with matrix-assisted laser desorption ionization—time of flight mass spectrometry (MALDI-TOF MS) technology, effective microcystin detection was achieved. The performances of MALDI-TOF MS, high-liquid-performance chromatography with diode array detector (HPLC-DAD) and enzyme-linked immunosorbent assays (ELISA) test were compared. The results obtained in this study suggest that the exploitation of an efficient bio-accumulator organism, coupled with a rapid and sensitive analytical method, can be a very useful strategy in monitoring programs for early and prompt risk management.

Thermal risk in semi-batch reactors: The epoxidation of soybean oil

Abstract The interest in the epoxidation of vegetable oils is constantly growing in the chemical industry. The most common process is based on the oxidation of the unsaturated bonds by peroxyacids generated in situ, in the water phase, using concentrated hydrogen peroxide and the corresponding organic acid in presence of a mineral acid as catalyst. The overall epoxidation reaction is highly exothermic, hence operating conditions should be always addressed by safety considerations related to the possibility of runaway reactions. In this paper, best operating conditions and safety considerations have been defined for a complex reaction network for the epoxidation of soybean oil, which includes the decomposition reactions of the performic acid generated during the epoxidation. Furthermore, sensitivity-based reactor stability criteria have been applied and compared, for the design of an adequate Early Warning Detection System for the process.

An Incremental Sensor Placement Optimization in a Large Real-World Water System

Supplying modern water systems in smart cities requires the ability to monitor water quality in the production plants and the distribution network. Protection against accidental or intentional events is usually based on an Early Warning Detection System (EWDS) to minimize the impact of any contamination [1]. A major issue is positioning online sensors along the water distribution network to ensure the best protection at minimum cost. Several contributions for tackling such problem have been proposed during the last decade, including a scientific challenge comparing 14 methodologies [2] and two reviews of about 150 articles [3,4]. Currently there is no consensus about algorithms or design objectives to use, and such a problem is NP-hard [5,6]. In this paper, a greedy approach is proposed to near-optimally solve the sensor placement problem dealing with large-scale water systems. This methodology is designed to integrate both the specificity of the studied network (expert/prior knowledge) and flexibility related to the uncertainty of the nature, time, and duration of contamination injections. The method is illustrated to minimize the expected fraction of the exposed population on the largest network in France (about 100,000 nodes, 600,000 connections, above 8,000km of pipes). Costly approaches in terms of computation time, such as MIP (Mixed-Integer Programming) and exhaustive search cannot scale to large networks. A sensitivity analysis is presented using the greedy approach on a sub-network which leads to choose the number of sufficient contamination simulations and the concentration threshold used to detect contaminations. The extensive experiments allow us to highlight the effectiveness and the rapidity of the proposed approach.

Underwater geophysical monitoring for European Multidisciplinary Seafloor and water column Observatories

We present a review of our work on data acquired by GEOSTAR-class (GEophysical and Oceanographic STation for Abyssal Research) observatories deployed at three EMSO (European Multidisciplinary Seafloor and water-column Observatory; http://www.emso-eu.org) sites in southern European waters where strong geo-hazards are present: the Western Iberian Margin, the Western Ionian Sea, the Marmara Sea, and the Marsili basin in the Tyrrhenian Sea. A procedure for multiparameter data quality control is described. Then we explain why the seafloor is an interesting observation point for geophysical parameters and how it differs from land sites. We consider four interesting geophysical phenomena found at the EMSO sites that are related to geo-hazard. In the first case, we show how unknown seismicity and landslides in the Western Ionian Sea were identified and roughly localised through a single-sensor analysis based on the seismometer. In the second case, we concentrate on the problem of near-coast tsunami generation and describe a Tsunami Early Warning Detection (TEWD) system, tested in the Western Iberian Margin and currently operating in real time at the Western Ionian site. In the third case, we consider two large volcanoes in the central Mediterranean area, Mt. Etna and the Marsili seamount. Signals from the seismometer and gravimeter recorded at the seafloor at 2100m b.s.l. show various phases of Mt. Etna's 2002–2003 eruption. For the less-known Marsili we illustrate how several indicators coming from different sensors point to hydrothermal activity. A vector magnetometer at the two volcanic sites helps identify the magnetic lithospheric depth. In the fourth and final case, we present a multiparameter analysis which was focused on finding possible correlations between methane seepage and seismic energy release in the Gulf of Izmit (Marmara Sea).

Research on Detection Performance of a Composite Bistatic Radars System

A composite bistatic radars system is put forward to availably enhance the performance of anti-stealthy ,anti-reconnaissance, anti-jamming and anti-destruction for monostatic radar. The process modeling and technical project is introduced in this paper, and the detection area and measurement accuracy of the composite bistatic radars system is analyzed on the T/R-R process modeling. Lastly, a set of simulated experiments for the detection performance of composite bistatic radars system is carried out. The result of the simulation can provide the basis for structure multi-input and multi-output scouting early-warning detection system in the complex electromagnetic environment.

Forehead-Mounted Reflectance Oximetry for In-Cockpit Hypoxia Early Detection and Warning

Effective hypoxia-related mishap prevention relies upon aircrew rapid recognition of hypoxia symptoms. The objectives of this experiment were twofold: to compare the effectiveness of a forehead-mounted reflectance oximeter and finger-mounted pulse oximeter for application in a hypoxia early warning detection system, and to determine whether the forehead-mounted sensor could be placed within an aviation helmet. Subjects donned an aviation flight mask and were instrumented with a forehead reflectance oximeter, a finger pulse oximeter, a blood pressure cuff, and a skin temperature sensor. Following instrumentation, subjects breathed ambient air for 10 min through the Reduced Oxygen Breathing Device (ROBD) to allow for acclimation. The baseline period was followed by one of two counterbalanced ascent profiles used to model rapid exposures to altitude. Data were collected at 1 Hz from both sensors for the duration of the protocol. Analyses indicated an exceptionally strong agreement between the forehead and finger sensors at all ranges of desaturation. The sensitivity data revealed that the forehead sensor was significantly faster when responding to rapid changes in SpO2 than the finger. The sensor was successfully integrated inside the helmet; however, once donned by the subject, there was considerable artifact due to pressure fluctuations. While these data may seem to suggest that the forehead sensor is accurate and sensitive to altitude induced changes in SpO2, major drawbacks exist for the technology utilized in the current study. Significant improvements aimed at diminishing noise, curbing motion artifact, and improving reliability are required to reduce errant measurements.

Screening Analysis for Hazard Assessment of Peroxides Decomposition

In this study, the analysis of the decomposition of four different peroxides (including hydrogen peroxide) by screening calorimetry is proposed. The objective is to analyze the decomposition reactions and evaluate the consequences in particular when the process undergoes to thermal explosion and may be the cause of incidents. Screening calorimetry data allow us to define the conditions and ranges of temperature and pressure evolved during the reaction. In the experimental apparatus used (a screening calorimeter), the experiments have been carried out safely, even when there is a rapid and large increase in temperature and pressure. Scanning and isothermal conditions has been investigated. An Early Warning Detection System (EWDS) for thermal runaway, based on the divergence criterion, has also been applied off-line to the experimental isothermal data, in order to evaluate the sensitivity of the method applied to both temperature and pressure profiles. The results of the application of the EWDS have been co...

Use of molecular markers for early warning detection of harmful algal blooms

Harmful Algal Blooms (HABs) are a biological hazard for, and can also result from, aquaculture. To minimise economic and environmental impacts, an early warning detection system is needed. In this paper, two most commonly used molecular techniques are introduced with applications and examples involving work on the putatively toxic dinoflagellate Pfiesteria piscicida. Unlike previous reviews giving comprehensive accounts of the detection of HABs, this paper focuses on the technical aspects of the methods in an attempt to provide information potentially helpful for those who have little or no prior experience in molecular techniques but wish to apply these techniques to species of their interest.

Reaction Inhibition as a Method for Preventing Thermal Runaway in Industrial Processes

AbstractSummary: In this work inhibitors were used to prevent runaway reactions during methylmethacrylate suspension polymerization processes. The main problem that may more frequently occur in chemical reactors, carrying out free radical polymerization reactions, is the loss of temperature control. The addition of an inhibitor during polymerization processes can be considered as a good method to stop or at the least slow down the reaction. In this work two inhibitors were used: hydroquinone and 1,4‐benzoquinone in a series of polymerization experiments. In order to identify situations that can lead to a runaway reaction, an early warning detection system based on the divergence criterion was used. When this system signalled an alarm, small amounts of inhibitor were added to the reaction mixture. The results showed that hydroquinone and 1,4‐benzoquinone behave slightly differently and the reactor temperature can be kept within safe limits.

Calorimetric study of the inhibition of runaway reactions during methylmethacrylate polymerization processes

Reaction inhibition was adopted as a method to halt runaway phenomena during polymerization experiments. Use of reaction calorimetry coupled with a particular system for early detection of the onset of runaway (early warning detection system) has allowed to investigate the behaviour of two substances that can influence the reaction rate: hydroquinone and 1,4-benzoquinone. The process studied was the free-radical polymerization of methylmethacrylate carried out under batch conditions in bulk or in emulsion. The results show that hydroquinone and 1,4-benzoquinone behave differently: the first is an inhibitor because it completely stops the process; the second behaves as a retarder and could be used industrially to control the process and keep the reactor temperature within safe limits.

Optimal Layout of Early Warning Detection Stations for Water Distribution Systems Security

Deliberate contamination is generally viewed as the most serious potential terrorist threat to water systems. Chemical or biological agents could spread throughout a distribution system and result in sickness or death among the people drinking the water. Since September 11, 2001 the U.S. Environmental Protection Agency's water protection task force and regional offices have initiated massive actions to improve the security of the drinking water infrastructure. A methodology is presented for finding the optimal layout of an early warning detection system ~EWDS!. The detection system is comprised of a set of monitoring stations aimed at capturing deliberate external terrorist hazard intrusions through water distribution system nodes—sources, tanks, and consumers. The optimization considers extended period unsteady hydraulics and water quality conditions for a given defensive level of service to the public, defined as a maximum volume of polluted water exposure at a concentration higher than a minimum hazard level. Such a scheme provides an EWDS for a deliberate terrorist external hazard intrusion, as well as for accidental contamination entries under unsteady conditions—a problem that currently has not been solved. The methodology is cast in a genetic algorithm framework for integration with EPANET and is demonstrated through two example applications. DOI: 10.1061/~ASCE!0733-9496~2004!130:5~377! CE Database subject headings: Water distribution; Monitoring; Optimization; Evolutionary computation; Water quality; Security; Terrorism.