The invention relates to a gas normal pressure sampling device suitable for vehicle-mounted measurement. The gas normal pressure sampling device is characterized by comprising a sampling outer pipe, a gas sampling pipe, a pressure sensor, a stepping motor and two porous plates, wherein the sampling outer pipe consists of a thin pipe and a thick pipe which are integrally arranged; the thin pipe and the thick pipe are connected by adopting a diffuse type structure; a branch pipe which extends downwards is arranged on a side wall of the thick pipe; one end of the gas sampling pipe penetrates through the branch pipe and is communicated with an inner cavity of the thick pipe; the other end of the gas sampling pipe is connected with a first end of a three-way joint, a second end of the three-way joint is connected with a sampling header pipe of a gas concentration detection instrument, and a third end of the three-way joint is connected with the pressure sensor; the pressure sensor is electrically connected with the stepping motor; the stepping motor is fixed on the porous plate positioned on the outer side, and a rotating shaft of the stepping motor penetrates through the porous plate positioned on the outer side and is rotationally connected with the porous plate positioned on the inner side. The gas normal pressure sampling device can be widely applied to gas sampling during aerial survey.

Gas normal pressure sampling device suitable for vehicle-mounted measurement

The present invention discloses a low flow air sampler, which comprises a main box, a sampling pipe and the like, wherein the sampling pipe comprises a sampling cap, a cutting head, a sampling chamber and a sampling column for entrapping air particulates having the diameter of more than a preset equivalent diameter, capturing the currently-sampled air particulates, and synchronously collecting a gas-state sample, and the main box comprises a housing, a direct current power supply, a LCD display screen, a flow meter, a flow rate control device for controlling and adjusting the current sampling flow rate, and a main control circuit for controlling the flow rate control device to adjust the current sampling flow rate according to the set sampling conditions so as to control the sampling pipe to perform air sampling. According to the present invention, the air particulates and the gas-state sample can be synchronously collected, wherein the air particulate samples with different equivalent diameters can be screened and collected by controlling the flow rate, selecting the cutting heads with different pore sizes, and replacing the filtration membranes with different pore sizes while the gas-state sample is synchronously collected by using the capturing column, and the sampling functions such as wind direction setting, wind speed setting, temperature setting, humidity setting and the like are provided.

Low flow air sampler

A fast and reliable nuclide identification algorithm is important to the detection of radioactive materials, and the emergence of a sequential Bayesian algorithm provides a new direction of research on radioactive detection. This article proposes a Bayesian nuclide identification algorithm based on the energy spectrum and deploys the algorithm on a microprocessor for real-time online processing. The algorithm builds a probability model based on the natural background radiation and radioactive source radiation and a decision function based on sequence testing. The processor conducts a posteriori estimation on the collected energy spectrum at fixed time intervals and updates the decision function. A laboratory self-developed CeBr3 detector was used in testing. Four radioactive sources, namely 241Am, 133Ba, 137Cs, and 60Co, were used in experiments with fixed and moving radioactive sources. The experimental results show that the proposed algorithm can rapidly identify mixed and mobile radioactive sources.

A Bayesian Nuclide Identification Algorithm Based on the Energy Spectrum

A fast and reliable nuclide identification algorithm is important to the detection of radioactive materials, and the emergence of a sequential Bayesian algorithm provides a new direction of research on radioactive detection. This article proposes a Bayesian nuclide identification algorithm based on the energy spectrum and deploys the algorithm on a microprocessor for real-time online processing. The algorithm builds a probability model based on the natural background radiation and radioactive source radiation and a decision function based on sequence testing. The processor conducts a posteriori estimation on the collected energy spectrum at fixed time intervals and updates the decision function. A laboratory self-developed CeBr3 detector was used in testing. Four radioactive sources, namely 241Am, 133Ba, 137Cs, and 60Co, were used in experiments with fixed and moving radioactive sources. The experimental results show that the proposed algorithm can rapidly identify mixed and mobile radioactive sources. 

Traditional radionuclide identification algorithms are based on gamma-ray spectrum analysis. A novel radionuclide identification method was proposed in this work and it was based on the combined use of the Hilbert–Huang Transform and a Convolutional Neural Network (HHT-CNN) and taken as input data gamma-ray pulses, instead of an entire gamma-ray spectrum. Firstly, HHT was used to analyze the nuclear pulse signal in frequency domain, and then the frequency in the instantaneous-frequency-edge spectrum was divided into multiple intervals, and the accumulation of amplitude in each interval was taken as the characteristic of the nuclear pulse signal. After constructing the data set with signal features, the one-dimensional Convolutional Neural Network (1D-CNN) model was established and trained to complete the nuclide recognition. In the simulated experiments, the model was tested by the nuclear pulse signals of 137Cs, 60Co, 152Eu, 133Ba, 40K, 60Co+137Cs, 60Co+152Eu and 137Cs+152Eu, which were simulated with the Geant4 toolkit and pulse signal simulator DT5800D (produced by CAEN Technologies Inc.). At the same time, the influence of pulse number and noise on the model recognition results was discussed. Finally, the model was applied to the actual experimental data to verify the model’s feasibility further. The results show that the proposed method can quickly and accurately identify radioactive nuclides. The study provides a new solution for radionuclides identification. 

Novel radionuclides identification method based on Hilbert–Huang Transform and Convolutional Neural Network with gamma-ray pulse signal

Radionuclide detection is a key step in the control of radioactive materials. Influenced by detector performance, ambient background noise and data analysis model, fast identification of radionuclides is a great challenge faced by traditional gamma-ray spectrometry analysis methods. This paper introduces the signal processing model and basic inspection strategy of nuclide recognition method based on statistical test, a Monte Carlo method to select the test threshold is proposed, and a radionuclide characteristic gamma-ray identifier for137Cs based on Wald sequential probability ratio test is constructed. In the end, the detection performance of the identifier is analyzed through emulate experiment.

Study on Sequential Bayesian Radionuclide Identification Approach: Threshold and Detection Capability

The utility model relates to an airborne air sampling device which comprises a filter, a gas flow tube, a vacuum pump and a gas flowmeter. The filter consists of a protective cap and a filter cartridge which are in threaded connection. A protruding edge is arranged on the end face of the protective cap. Sampling holes in the filter are circularly formed in the side wall of the protective cap. According to the filter in the device, the sampling holes are circularly formed in the side wall of the protective cap and can be matched with the protective cap provided with the protruding edge, so that filter paper and an active carbon box are prevented from absorbing ineffective aerosol and micromolecular particles due to the strong airflow effect even if the vacuum pump is not started in the high-speed movement process of an airplane so as to ensure the accuracy of samples. Therefore, the airborne air sampling device provided by the utility model can satisfy the design of special operating requirements of unmanned helicopters.

Airborne air sampling device

The accurate and rapid identification of radionuclide is an extremely important matter of radioactive material control and public security check. The classical detection method using γ-ray spectrum depends on large numbers of γ-ray measurements, which means a long time cost, especially when the radioactivity is low. A physics-based radionuclide γ-ray event modes sequence, representing specific nuclide is introduced, which offers a pattern recognition approach to attack this problem. The development of radionuclide representation in terms of characteristic γ-ray energy along with branching ratio that utilizes the measurement of photon energies by the detector and the probable γ-ray emission is described. The Kalman filtering is used to estimate the underlying characteristic γ-ray energy parameter in specific energy interval related to interested nuclides from the uncertain measurement photon by photon in this paper. The implementation of this approach is applied to experimental data to demonstrate its validity. Keywords—radionuclide detection; physics-based approach; event sequence mode; Kalman filtering

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Lijun Zhang

Papers

Study on Sequential Bayesian Radionuclide Identification Approach: Threshold and Detection Capability

Airborne air sampling device

Physics-based Radionuclide Event Mode Sequence and Radioactive Pattern Analysis