Detecting explosives by PGNAA using KNN Regressors and decision tree classifier: A proof of concept

Hossny, K.; Magdi, S.; Soliman, Abdelfattah Y.; Hossny, Ahmad Hany

doi:10.1016/j.pnucene.2020.103332

Cited by 18 publications

(11 citation statements)

References 11 publications

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“…This is because reinforcement learning methods, especially the off-policy ones, rely on previous experiences during training. These advantages do allow for more stability in deploying DRL models in critical applications, such as nuclear engineering [35][36][37][38].…”

Section: Discussionmentioning

confidence: 99%

Refined Continuous Control of DDPG Actors via Parametrised Activation

Hossny¹,

Iskander²,

Attia³

et al. 2021

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Continuous action spaces impose a serious challenge for reinforcement learning agents. While several off-policy reinforcement learning algorithms provide a universal solution to continuous control problems, the real challenge lies in the fact that different actuators feature different response functions due to wear and tear (in mechanical systems) and fatigue (in biomechanical systems). In this paper, we propose enhancing the actor-critic reinforcement learning agents by parameterising the final layer in the actor network. This layer produces the actions to accommodate the behaviour discrepancy of different actuators under different load conditions during interaction with the environment. To achieve this, the actor is trained to learn the tuning parameter controlling the activation layer (e.g., Tanh and Sigmoid). The learned parameters are then used to create tailored activation functions for each actuator. We ran experiments on three OpenAI Gym environments, i.e., Pendulum-v0, LunarLanderContinuous-v2, and BipedalWalker-v2. Results showed an average of 23.15% and 33.80% increase in total episode reward of the LunarLanderContinuous-v2 and BipedalWalker-v2 environments, respectively. There was no apparent improvement in Pendulum-v0 environment but the proposed method produces a more stable actuation signal compared to the state-of-the-art method. The proposed method allows the reinforcement learning actor to produce more robust actions that accommodate the discrepancy in the actuators’ response functions. This is particularly useful for real life scenarios where actuators exhibit different response functions depending on the load and the interaction with the environment. This also simplifies the transfer learning problem by fine-tuning the parameterised activation layers instead of retraining the entire policy every time an actuator is replaced. Finally, the proposed method would allow better accommodation to biological actuators (e.g., muscles) in biomechanical systems.

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Section: Discussionmentioning

confidence: 99%

Refined Continuous Control of DDPG Actors via Parametrised Activation

Hossny¹,

Iskander²,

Attia³

et al. 2021

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“…3. Dimensions of the setup are listed in Supplementary Table 2 22,31 . The gamma rays emitted due to neutron interactions with the sample travel in different directions.…”

Section: Data Generationmentioning

confidence: 99%

“…Hence, changing the orientation of the investigated sample will not affect the resulting gamma spectrum. We used our previously developed and validated MCNP model with validation metrics listed in Supplementary Table 3 22 . In the developed MCNP model that we used in the data generation process, we didn't consider the natural radioactivity background.…”

Section: Data Generationmentioning

confidence: 99%

“…One other drawback of using PGNAA in explosives detection is the need for a skilled operator to build a decision based on the system’s results 21 . Using machine learning regressors and classifiers such as K-nearest neighbor (KNN) regressors and decision tree classifier to analyze the gamma spectra resulted in 92% accuracy in differentiating between explosive and non-explosive hydrocarbons 22 .

Figure 1 Sample of PGNAA obtained gamma spectra for TNT when shielded with boron.…”

Section: Introductionmentioning

confidence: 99%

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Detecting shielded explosives by coupling prompt gamma neutron activation analysis and deep neural networks

Hossny

Hossny²,

Magdi

et al. 2020

Sci Rep

Self Cite

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Prompt Gamma Neutron Activation Analysis is a nuclear-based technique that can be used in explosives detection. It relies on bombarding unknown samples with neutrons emitted from a neutron source. These neutrons interact with the sample nuclei emitting the gamma spectrum with peaks at specific energies, which are considered a fingerprint for the sample composition. Analyzing these peaks heights will give information about the unknown sample material composition. Shielding the sample from gamma rays or neutrons will affect the gamma spectrum obtained to be analyzed, providing a false indication about the sample constituents, especially when the shield is unknown. Here we show how using deep neural networks can solve the shielding drawback associated with the prompt gamma neutron activation analysis technique in explosives detection. We found that the introduced end-to-end framework was capable of differentiating between explosive and non-explosive hydrocarbons with accuracy of 95% for the previously included explosives in the model development data set. It was also, capable of generalizing with accuracy 80% over the explosives which were not included in the model development data set. Our results show that coupling prompt gamma neutron activation analysis with deep neural networks has a good potential for high accuracy explosives detection regardless of the shield presence.

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“…Various detection techniques, equipment and methods are used for different types of explosive hazardous chemicals. Conventional methods include gas chromatography, liquid chromatography, ion chromatography, mass spectrometry, isotope ratio mass spectrometry, capillary electrophoresis, thermal analysis (thermogravimetry (TG) and differential scanning calorimetry (DSC)), molecular imprinting, general spectroscopic methods (fluorescence, luminescence, spectrophotometry, ultraviolet, and chemiluminescence), Fourier transform infrared spectroscopy, and Raman spectroscopy [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. For instance, Catherine E. Hay, et al [2] proposed a simple and robust, low-cost electrochemical device for the combined sampling and detection of the trace solid explosive 2,4,6-trinitrotoluene (TNT) from a non-porous surface, and the prototype device was able to detect TNT with a 30 min development time in different ambient environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Identification of Typical Solid Hazardous Chemicals Based on Hyperspectral Imaging

et al. 2021

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The identification of hazardous chemicals based on hyperspectral imaging is an important emergent means for the prevention of explosion accidents and the early warning of secondary hazards. In this study, we used a combination of spectral curve matching based on full-waveform characteristics and spectral matching based on spectral characteristics to identify the hazardous chemicals, and proposed a method to quantitatively characterize the matching degree of the spectral curves of hazardous chemicals. The results showed that the four hazardous chemicals, sulfur, red phosphorus, potassium permanganate, and corn starch had bright colors, distinct spectral curve characteristics, and obvious changes in reflectivity, which were easy to identify. Moreover, the matching degree of their spectral curves was positively correlated with their reflectivity. However, the spectral characteristics of carbon powder, strontium nitrate, wheat starch, and magnesium–aluminum alloy powder were not obvious, with no obvious characteristic peaks or trends of change in reflectivity. Except for the reflectivity and the matching degree of the carbon powder being maintained at a low level, the reflectivity of the remaining three samples was relatively close, so that it was difficult to identify with the spectral curves alone, and color information should be considered for further identification.

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Detecting explosives by PGNAA using KNN Regressors and decision tree classifier: A proof of concept

Cited by 18 publications

References 11 publications

Refined Continuous Control of DDPG Actors via Parametrised Activation

Refined Continuous Control of DDPG Actors via Parametrised Activation

Detecting shielded explosives by coupling prompt gamma neutron activation analysis and deep neural networks

Identification of Typical Solid Hazardous Chemicals Based on Hyperspectral Imaging

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