A Dynamic Framework for Internet-Based Network Time Protocol

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary.

Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting

This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE).

The Simulation of In-Situ Groundwater Detector Response as a Means of Identifying Beta Emitting Radionuclides by Linear Regression Analysis

The in-situ characterisation of strontium-90 contamination of groundwater at nuclear decommissioning sites would represent a novel and cost-saving technology for the nuclear industry. However, beta particles are emitted over a continuous spectrum and it is difficult identify radionuclides due to the overlap of their spectra and the lack of characteristic features. This can be resolved by using predictive modelling to perform a maximum-likelihood estimation of the radionuclides present in a beta spectrum obtained with a semiconductor detector. This is achieved using a linear least squares linear regression and relating experimental data with simulated detector response data. In this case, by simulating a groundwater borehole scenario and the deployment of a cadmium telluride detector within it, it is demonstrated that it is possible to identify the presence of 90Sr, 90Y, 137Cs and 235U decay. It is determined that the optimal thickness of the CdTe detector for this technique is in the range of 0.1 to 1 mm. The influence of suspended solids in the groundwater is also investigated. The average and maximum concentrations of suspended particles found at Sellafield do not significantly deteriorate the results. It is found that applying the linear regression over two energy windows improves the estimate of 90Sr activity in a mixed groundwater source. These results provide validation for the ability of in-situ detectors to determine the activity of 90Sr in groundwater in a timely and cost-effective manner.

Integration of Ground- Penetrating Radar and Gamma-Ray Detectors for Nonintrusive Characterisation of Buried Radioactive Objects

The characterisation of buried radioactive wastes is challenging because they are not readily accessible. Therefore, this study reports on the development of a method for integrating ground-penetrating radar (GPR) and gamma-ray detector measurements for nonintrusive characterisation of buried radioactive objects. The method makes use of the density relationship between soil permittivity models and the flux measured by gamma ray detectors to estimate the soil density, depth and radius of a disk-shaped buried radioactive object simultaneously. The method was validated using numerical simulations with experimentally-validated gamma-ray detector and GPR antenna models. The results showed that the method can simultaneously retrieve the soil density, depth and radius of disk-shaped radioactive objects buried in soil of varying conditions with a relative error of less than 10%. This result will enable the development of an integrated GPR and gamma ray detector tool for rapid characterisation of buried radioactive objects encountered during monitoring and decontamination of nuclear sites and facilities.

Gas Flow to Enhance the Detection of Alpha-Induced Air Radioluminescence Based on a UVTron Flame Sensor

In many field applications where alpha-induced radioluminescence (or so-called UV fluorescence) could potentially be used for stand-off detection of alpha-emitting materials, it may not be possible to create a fully purged gas atmosphere. Hence, an alternative gas delivery method to utilise the radioluminescence enhancing properties of gases has been investigated, with the novel results from this presented herewithin. A solar blind ultraviolet C (UVC) sensor (UVTron R9533, Hamamatsu, Japan) has been used to detect changes in the signal in the UVC wavelength range (180⁻280 nm), where gases of Ar, Xe, Ne, N₂, Kr, and P-10 were flowed over a 6.95 MBq ²¹⁰Po source using a narrow diameter pipe close to the source. In comparison with an air atmosphere, there was an increase in signal in all instances, the greatest being the flow of Xe, which in one instance greater than doubled the average counts per second. This increase in signal could prove beneficial in the design of a stand-off alpha detector to detect the very small UVC radioluminescence signals from alpha-emitting materials found in nuclear decommissioning environments.

Alpha Particle Detection Using Alpha-Induced Air Radioluminescence: A Review and Future Prospects for Preliminary Radiological Characterisation for Nuclear Facilities Decommissioning

The United Kingdom (UK) has a significant legacy of nuclear installations to be decommissioned over the next 100 years and a thorough characterisation is required prior to the development of a detailed decommissioning plan. Alpha radiation detection is notoriously time consuming and difficult to carry out due to the short range of alpha particles in air. Long-range detection of alpha particles is therefore highly desirable and this has been attempted through the detection of secondary effects from alpha radiation, most notably the air-radioluminescence caused by ionisation. This paper evaluates alpha induced air radioluminescence detectors developed to date and looks at their potential to develop a stand-off, alpha radiation detector which can be used in the nuclear decommissioning field in daylight conditions to detect alpha contaminated materials.

First Results of Using a UVTron Flame Sensor to Detect Alpha-Induced Air Fluorescence in the UVC Wavelength Range

In this work, a robust stand-off alpha detection method using the secondary effects of alpha radiation has been sought. Alpha particles ionise the surrounding atmosphere as they travel. Fluorescence photons produced as a consequence of this can be used to detect the source of the alpha emissions. This paper details experiments carried out to detect this fluorescence, with the focus on photons in the ultraviolet C (UVC) wavelength range (180-280 nm). A detector, UVTron R9533 (Hamamatsu, 325-6, Sunayama-cho, Naka-ku, Hamamatsu City, Shizuoka Pref., 430-8587, Japan), designed to detect the UVC emissions from flames for fire alarm purposes, was tested in various gas atmospheres with a ²¹⁰Po alpha source to determine if this could provide an avenue for stand-off alpha detection. The results of the experiments show that this detector is capable of detecting alpha-induced air fluorescence in normal indoor lighting conditions, as the interference from daylight and artificial lighting is less influential on this detection system which operates below the UVA and UVB wavelength ranges (280-315 nm and 315-380 nm respectively). Assuming a standard 1 r 2 drop off in signal, the limit of detection in this configuration can be calculated to be approximately 240 mm, well beyond the range of alpha-particles in air, which indicates that this approach could have potential for stand-off alpha detection. The gas atmospheres tested produced an increase in the detector count, with xenon having the greatest effect with a measured 52% increase in the detector response in comparison to the detector response in an air atmosphere. This type of alpha detection system could be operated at a distance, where it would potentially provide a more cost effective, safer, and faster solution in comparison with traditional alpha detection methods to detect and characterise alpha contamination in nuclear decommissioning and security applications.

Ground Penetrating Radar as a Contextual Sensor for Multi-Sensor Radiological Characterisation

Radioactive sources exist in environments or contexts that influence how they are detected and localised. For instance, the context of a moving source is different from a stationary source because of the effects of motion. The need to incorporate this contextual information in the radiation detection and localisation process has necessitated the integration of radiological and contextual sensors. The benefits of the successful integration of both types of sensors is well known and widely reported in fields such as medical imaging. However, the integration of both types of sensors has also led to innovative solutions to challenges in characterising radioactive sources in non-medical applications. This paper presents a review of such recent applications. It also identifies that these applications mostly use visual sensors as contextual sensors for characterising radiation sources. However, visual sensors cannot retrieve contextual information about radioactive wastes located in opaque environments encountered at nuclear sites, e.g., underground contamination. Consequently, this paper also examines ground-penetrating radar (GPR) as a contextual sensor for characterising this category of wastes and proposes several ways of integrating data from GPR and radiological sensors. Finally, it demonstrates combined GPR and radiation imaging for three-dimensional localisation of contamination in underground pipes using radiation transport and GPR simulations.

A novel approach to neutron dosimetry

PURPOSE

Having been overlooked for many years, research is now starting to take into account the directional distribution of neutron workplace fields. Existing neutron dosimetry instrumentation does not account for this directional distribution, resulting in conservative estimates of dose in neutron workplace fields (by around a factor of 2, although this is heavily dependent on the type of field). This conservatism could influence epidemiological studies on the health effects of radiation exposure. This paper reports on the development of an instrument which can estimate the effective dose of a neutron field, accounting for both the direction and the energy distribution.

METHODS

A ⁶Li-loaded scintillator was used to perform neutron assays at a number of locations in a 20 × 20 × 17.5 cm³ water phantom. The variation in thermal and fast neutron response to different energies and field directions was exploited. The modeled response of the instrument to various neutron fields was used to train an artificial neural network (ANN) to learn the effective dose and ambient dose equivalent of these fields. All experimental data published in this work were measured at the National Physical Laboratory (UK).

RESULTS

Experimental results were obtained for a number of radionuclide source based neutron fields to test the performance of the system. The results of experimental neutron assays at 25 locations in a water phantom were fed into the trained ANN. A correlation between neutron counting rates in the phantom and neutron fluence rates was experimentally found to provide dose rate estimates. A radionuclide source behind shadow cone was used to create a more complex field in terms of energy and direction. For all fields, the resulting estimates of effective dose rate were within 45% or better of their calculated values, regardless of energy distribution or direction for measurement times greater than 25 min.

CONCLUSIONS

This work presents a novel, real-time, approach to workplace neutron dosimetry. It is believed that in the research presented in this paper, for the first time, a single instrument has been able to estimate effective dose.

A Monte Carlo study of the effect of coded-aperture material and thickness on neutron imaging

In this paper, a coded-aperture design for a scintillator-based neutron imaging system has been simulated using a series of Monte Carlo simulations. Using Monte Carlo simulations, work to optimise a system making use of the EJ-426 neutron scintillator detector has been conducted. This type of scintillator has a low sensitivity to gamma rays and is therefore particularly useful for neutron detection in a mixed radiation environment. Simulations have been conducted using varying coded-aperture materials and different coded-aperture thicknesses. From this, neutron images have been produced, compared qualitatively and quantitatively for each case to find the best material for the MURA (modified uniformly redundant array) pattern. The neutron images generated also allow observations on how differing thicknesses of coded-aperture impact the system.

A digital approach to neutron-γ imaging with a narrow tungsten collimator aperture and a fast organic liquid scintillator detector

A digital neutron-γ imaging technique for mixed radiation field is described. The imaging system is based upon an organic liquid scintillator detector, a narrow tungsten collimator, fast digitiser and adjustable equatorial mount. Radioactive sources have been attached to a vertical plane and the digitiser has been used to digitise neutron and γ events. The digitised events have been discriminated using pulse gradient analysis and images have been generated of the count distribution in the source plane.