Potential of a laser-driven source of characteristic γ-rays for fast detection and identification of nuclear materials

Abstract We propose linear parameter-varying (LPV) model-based approaches to the synthesis of robust fault detection and diagnosis (FDD) systems for loss of efficiency (LOE) faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative) LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults) is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.

Download Full-text

Near-Complete Genome Sequence of a Human Norovirus GII.1[Pg] Strain Associated with Acute Gastroenteritis, Determined Using Long-Read Sequencing

Microbiology Resource Announcements ◽

10.1128/mra.00401-21 ◽

2021 ◽

Vol 10 (28) ◽

Author(s):

Zhihui Yang ◽

Mark Mammel ◽

Samantha Q. Wales

Keyword(s):

Foodborne Pathogens ◽

High Throughput Sequencing ◽

Read Length ◽

Human Norovirus ◽

Short Read ◽

Fast Detection ◽

Short Read Sequencing ◽

Detection And Identification ◽

Long Read ◽

Norovirus Gii

High-throughput sequencing is one of the approaches used for the detection of foodborne pathogens such as noroviruses. Long-read sequencing has advantages over short-read sequencing in speed, read length, and lower fragmentation bias, which makes it a potential powerful tool for the fast detection and identification of viruses.

Download Full-text

Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation

International Journal of Monitoring and Surveillance Technologies Research ◽

10.4018/ijmstr.2016100104 ◽

2016 ◽

Vol 4 (4) ◽

pp. 54-64 ◽

Cited By ~ 1

Author(s):

Pola Lydia Lagari ◽

Vladimir Sobes ◽

Miltiadis Alamaniotis ◽

Lefteri H. Tsoukalas

Keyword(s):

Modeling And Simulation ◽

Cross Sections ◽

Experimental Testing ◽

Detection Limits ◽

Nuclear Data ◽

Nuclear Materials ◽

Simulation Techniques ◽

Detection And Identification ◽

The Us ◽

Nuclear Systems

Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Consequently, modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.

Download Full-text

Ship Target Detection Algorithm Based on Improved YOLOv5

Journal of Marine Science and Engineering ◽

10.3390/jmse9080908 ◽

2021 ◽

Vol 9 (8) ◽

pp. 908

Author(s):

Junchi Zhou ◽

Ping Jiang ◽

Airu Zou ◽

Xinglin Chen ◽

Wenwu Hu

Keyword(s):

Visual Detection ◽

Detection System ◽

Recall Rate ◽

Detection Algorithm ◽

Average Precision ◽

Fast Detection ◽

Detection And Identification ◽

Small Targets ◽

Improved Model ◽

Field Area

In order to realize the real-time detection of an unmanned fishing speedboat near a ship ahead, a perception platform based on a target visual detection system was established. By controlling the depth and width of the model to analyze and compare training, it was found that the 5S model had a fast detection speed but low accuracy, which was judged to be insufficient for detecting small targets. In this regard, this study improved the YOLOv5s algorithm, in which the initial frame of the target is re-clustered by K-means at the data input end, the receptive field area is expanded at the output end, and the loss function is optimized. The results show that the precision of the improved model’s detection for ship images was 98.0%, and the recall rate was 96.2%. Mean average precision (mAP) reached 98.6%, an increase of 4.4% compared to before the improvements, which shows that the improved model can realize the detection and identification of multiple types of ships, laying the foundation for subsequent path planning and automatic obstacle avoidance of unmanned ships.

Download Full-text

Advances in Chemical and Biological Methods to Identify Microorganisms—From Past to Present

Microorganisms ◽

10.3390/microorganisms7050130 ◽

2019 ◽

Vol 7 (5) ◽

pp. 130 ◽

Cited By ~ 29

Author(s):

Ricardo Franco-Duarte ◽

Lucia Černáková ◽

Snehal Kadam ◽

Karishma S. Kaushik ◽

Bahare Salehi ◽

...

Keyword(s):

Ribosomal Rna ◽

Culture Media ◽

Low Cost ◽

Molecular Techniques ◽

Significant Feature ◽

Biochemical Tests ◽

Fast Detection ◽

Detection And Identification ◽

Polymerase Chain ◽

Biological Methods

Fast detection and identification of microorganisms is a challenging and significant feature from industry to medicine. Standard approaches are known to be very time-consuming and labor-intensive (e.g., culture media and biochemical tests). Conversely, screening techniques demand a quick and low-cost grouping of bacterial/fungal isolates and current analysis call for broad reports of microorganisms, involving the application of molecular techniques (e.g., 16S ribosomal RNA gene sequencing based on polymerase chain reaction). The goal of this review is to present the past and the present methods of detection and identification of microorganisms, and to discuss their advantages and their limitations.

Download Full-text