The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring suffi...The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.展开更多
Bottom simulating reflector(BSR)has been recognized as one of the indicators of gas hydrates.However,BSR and hydrate are not one-to-one correspondence.In the Xisha area of South China Sea(SCS),carbonate rocks wildly d...Bottom simulating reflector(BSR)has been recognized as one of the indicators of gas hydrates.However,BSR and hydrate are not one-to-one correspondence.In the Xisha area of South China Sea(SCS),carbonate rocks wildly develop,which continuously distribute parallel to the seafloor with high amplitude on seismic sections,exhibiting reflections similar to BSRs in the Shenhu area nearby.This phenomenon causes some interference to hydrates identification.In this paper,the authors discussed the typical geophysical differences between carbonate rocks and hydrates,indicating that the main difference exists in relationship between porosity and velocity,causing different amplitude versus offset(AVO)characters.Then the authors proposed a new model assuming that the carbonates form the matrix and the hydrate fill the pore as a part of the matrix.The key modeling parameters have been optimized constrained by Pvelocities and S-velocities simultaneously,and the model works well both for carbonate rock and gas hydrate bearing sediments.For quantitative identification,the authors calculated the velocities when carbonates and hydrates form the matrix together in different proportions.Then they proposed a carbonate and hydrate identification template(CHIT),in which the possible hydrate saturation(PHS)and possible carbonate content(PCC)can be both scaled out for a group of sample composed by P-velocity and S-velocity.If PHS is far larger than PCC,it is more likely to be a hydrate sample because carbonates and hydrates do not coexist normally.The real data application shows that the template can effectively distinguish between hydrates and carbonate rocks,consequently reducing the risk of hydrate exploration.展开更多
To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range ...To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris samples.The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.展开更多
Temperature prediction plays an important role in ring die granulator control,which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonline...Temperature prediction plays an important role in ring die granulator control,which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonlinear, and large time-delay characteristics. Support vector machine( SVM) has been successfully based on small data. But its accuracy is not high,in contrast,if the number of data and dimension of feature increase,the training time of model will increase dramatically. In this paper,a linear SVM was applied combing with cyclic coordinate descent( CCD) to solving big data regression. It was mathematically strictly proved and validated by simulation. Meanwhile,real data were conducted to prove the linear SVM model's effect. Compared with other methods for big data in simulation, this algorithm has apparent advantage not only in fast modeling but also in high fitness.展开更多
基金supported by the Program for NIM-Basic Research Business Expenses Key Field Program,China(No.AKYCX2315).
文摘The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.
基金the China Geological Survey Program(DD20190217)2018 Open Fund Project of Key Laboratory of Submarine Mineral Resources,Ministry of Natural Resources(KLMMR-2018-A-04)Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(GML2019ZD0102)
文摘Bottom simulating reflector(BSR)has been recognized as one of the indicators of gas hydrates.However,BSR and hydrate are not one-to-one correspondence.In the Xisha area of South China Sea(SCS),carbonate rocks wildly develop,which continuously distribute parallel to the seafloor with high amplitude on seismic sections,exhibiting reflections similar to BSRs in the Shenhu area nearby.This phenomenon causes some interference to hydrates identification.In this paper,the authors discussed the typical geophysical differences between carbonate rocks and hydrates,indicating that the main difference exists in relationship between porosity and velocity,causing different amplitude versus offset(AVO)characters.Then the authors proposed a new model assuming that the carbonates form the matrix and the hydrate fill the pore as a part of the matrix.The key modeling parameters have been optimized constrained by Pvelocities and S-velocities simultaneously,and the model works well both for carbonate rock and gas hydrate bearing sediments.For quantitative identification,the authors calculated the velocities when carbonates and hydrates form the matrix together in different proportions.Then they proposed a carbonate and hydrate identification template(CHIT),in which the possible hydrate saturation(PHS)and possible carbonate content(PCC)can be both scaled out for a group of sample composed by P-velocity and S-velocity.If PHS is far larger than PCC,it is more likely to be a hydrate sample because carbonates and hydrates do not coexist normally.The real data application shows that the template can effectively distinguish between hydrates and carbonate rocks,consequently reducing the risk of hydrate exploration.
基金supported by the National Key Research and Development Program of China(No.2019YFC1520701)National Natural Science Foundation of China(Nos.61965015,61741513)+2 种基金the 2020 Industry Support Plan Project in University of Gansu Province(No.2020C-17)the Young Teachers Scientific Research Ability Promotion Plan of Northwest Normal University Province(No.NWNW-LKQN2019-1)the Funds for Innovative Fundamental Research Group Project of Gansu Province(No.21JR7RA131)。
文摘To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris samples.The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.
基金Nantong Research Program of Application Foundation,China(No.BK2012030)Key Project of Science and Technology Commission of Shanghai Municipality,China(No.10JC1405000)
文摘Temperature prediction plays an important role in ring die granulator control,which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonlinear, and large time-delay characteristics. Support vector machine( SVM) has been successfully based on small data. But its accuracy is not high,in contrast,if the number of data and dimension of feature increase,the training time of model will increase dramatically. In this paper,a linear SVM was applied combing with cyclic coordinate descent( CCD) to solving big data regression. It was mathematically strictly proved and validated by simulation. Meanwhile,real data were conducted to prove the linear SVM model's effect. Compared with other methods for big data in simulation, this algorithm has apparent advantage not only in fast modeling but also in high fitness.
