Query: Application of neural netw ork techniques in gamma spectroscopy

Candidate papers:

abstract: Abstract The usual methods of automatic radiation spectra analysis, based on fittings of peaks and background to exact mathematical curves, are valid for high resolution detectors. However, these methods are less successful for lower resolution detectors, such as the common scintillators or new room temperature semiconductors. Trying to solve some of the problems inherent in the application of complex fittings to the response of these detectors, we test here a new and less strict approach, based on the use of a neural network algorithm known as "associative memory" This method appears useful in those cases in which a simple operation and a fast response are needed, together with a reasonable (and not extreme) accuracy. Furthermore, as the pattern recognition is carried out through the rough shape of the whole spectrum, instead of each individual peak, it can be used with advantage for low resolution detectors. With the idea of comparing the behavior of this method with the "classical" ones, the response of the network in the analysis of several spectra, taken with a NaI spectrometer, is presented.
author_names: P Olmos

J Casanueva Diaz

J Mercado Perez

Germa Garcia-Belmonte

P Gomez

Victoria Rodellar
title: Application of neural network techniques in gamma spectroscopy
year: 1992

abstract: The aim of this study was to investigate the potential for quantitative assessment of amylose and protein content in rice after gamma irradiation using infrared spectroscopy and chemometrics. Rice was treated with eight different radiation doses (250, 500, 750, 1000, 1500, 2000, 2500, and 3000 Gy) and compared to untreated rice (i.e. 0 Gy) Near infrared (NIR; 1100 2500 nm) and mid infrared (MIR; 400 4000 cm 1) spectra of the rice were compared to determine which one produced the best prediction of components for irradiated rice. Least squares support vector machine (LS SVM) was applied to construct calibration models for component analysis of amylose and protein individually. The optimal results built by LS SVM were obtained when the rp and RMSEP values were 0.8514 and 0.1519, respectively, for prediction of amylose in the NIR region and 0.8824 and 0.2012, respectively, for prediction of protein in the MIR region. Chemometrics based on LS SVM are better than that of a back propagation artificial neural network (BP ANN) This work demonstrates the potential of infrared reflectance spectroscopy using NIR and MIR for more efficient analysis of components in irradiated rice.
author_names: Yongni Shao

C F Zhao

Yong He

Yidan Bao
title: Application of Infrared Spectroscopy Technique and Chemometrics for Measurement of Components in Rice after Radiation
year: 2009

abstract: In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma ray spectrometer with an ANN. In this system the ANN is used to automatically identify, radioactive isotopes in real time from their gamma ray spectra. Two neural network paradigms are examined: the linear perception and the optimal linear associative memory (OLAM) A comparison of the two paradigms shows that OLAM is superior to linear perception for this application. Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the whole more spectrum in the identification process instead of only the individual photo peaks. For this reason, it is potentially more useful for processing data from lower resolution gamma ray spectrometers. This approach has been tested with data generated by Monte Carlo simulations and with field data from sodium iodide and Germanium detectors. With the ANN approach, the intense computation takes place during the training process. Once the network is trained, normal operation consists of propagating the data through the network, which results in rapid identification of samples. This approach is useful in situations that require fast response where precise quantification is less important. less
author_names: Paul E Keller

Lars J Kangas

Sherif Hashem

Richard T Kouzes
title: Neural networks for nuclear spectroscopy
year: 1995

abstract: With the rapid development of Internet of Things (IoT) techn iques, RF sensing has found wide applications for, e.g. indoor localization, activit y recognition, and healthcare. In this dissertation, we investigate the problem of RF sensing for IoT using channel state information (CSI) and machine learning techniques. In particular, our work mainl y focuses on indoor localization using deep learning and vital sign monitoring for RF sensing. In this dissertation, we first study the problem of CSI based in door localization. For first three works, we exploit deep learning for three different in door localization systems using CSI amplitudes, CSI calibrated phases, and CSI bimodal data, resp ectively. Moreover, we study and analyze CSI data, which is stable for indoor localization. We consider deep autoencoder networks to train CSI data, and employ the weights of the deep netw ork to represent fingerprints. A greedy learning algorithm is leveraged to train the weights layer by layer to reduce computational complexity, where a sub network between two consecutive la y rs forms a Restricted Boltzmann Machine (RBM) In the online stage, we use a probabilistic meth od for online location estimation. Then, we exploit deep convolutional neural networks (DCNN) f or indoor localization. Since DCNN is a supervised method, it only requires to train one grou p f weights for all the training data with related labels, which is different with our prior w orks that requires training weights for every training location. Specially, we use estimated angle of arrival (AOA) images from CSI data as input to the DCNN. By executing four convolutional and subsa mpling layers, the system can automatically extract the features of the estimated AOA ima ges, to obtain training weights. To improve indoor localization accuracy, we propose deep resi dual sharing learning for training two channels CSI tensor data. Moreover, we can stack many residua l haring blocks for adding the depth of the deep network, thus achieving higher learning an d representation ability for CSI tensor
author_names: Xuyu Wang
title: RF Sensing for Internet of Things: When Machine Learning Meets Channel State Information
year: 2018

abstract: In the imaging process of the remote sensing ,there was degradation phenomenon in the acquired images. In order to reduce the image blur caused by the degradation, the remote sensing images were restored to give prominence to the characteristic objects in the images.the images were restored. IMAGE restoration is an important issue in high level image processing..The purpose of image restoration is to estimate the original image from the degraded data. It is widely used in various f ields of applications, such as medical imaging, astronomical imaging, remote sensing, microscopy imaging, photography deblurring, and forensic science, etc. Restoration is beneficial to interpreting and analyzing the remote sensing images. After restoration, the blur phenomenon of the images is reduced. The characters are highlighted, and the visual effec t of the images is clearer. In this paper different image restoration techniques like Richardson Lucy algorithm, Wiener f ilter, Neural Netw ork,Blind Deconvolution. Keywords Image Restoration,Degradation model, Richardson Lucy algorithm,Wiener f ilter, Neural Netw ork,Blind Deconvolution.
author_names: Ajay Kaushik
title: Remote Sensing Image Restoration Using Various Techniques: A Review
year: 2012

abstract: Free unfolding in neutron spectroscopy means reconstructing energy spectra from experimental data without a priori assumptions regarding their shape. Due to the ill conditioned nature of the problem, this cannot be done analytically. Neural Networks (NNs) were applied to this task and synthetic data was used for training and testing. Results showed very consistent performance especially in the region of low and medium counts, where they fall near the Poisson statistical boundary. Comparison with other unfolding methods validated these results. Application time on the order of ms makes NNs suitable for real time analysis. This approach can be applied to any instrument of which the response function is known.
author_names: Emanuel Ronchi

Sean Conroy

Erik Andersson Sunden

G Ericsson

Maria Gatu Johnson

Carl Hellesen

Henrik Sjostrand

Matthias Weiszflog

J E T Contributors
title: Applications of neural networks for free unfolding of experimental data from fusion neutron spectrometers
year: 2008

abstract: Abstract Tomographic reconstruction techniques typically require computationally intensive algorithms which are not suitable for real time application. This paper describes a framework to perform neutron emissivity tomography at the Joint European Torus (JET) using neural networks with successful results over a broad range of magnetic configurations, heating and fueling schemes. Application times in the m s time scale allows for real time applicability of the method.
author_names: Emanuel Ronchi

Sean Conroy

Erik Andersson Sunden

G Ericsson

M Gatu Johnson

Carl Hellesen

Henrik Sjostrand

Matthias Weiszflog
title: Neural networks based neutron emissivity tomography at JET with real time capabilities
year: 2010

abstract: In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma ray spectrometer with an ANN to automatically identify radioactive isotopes in real time. Two neural network paradigms are examined and compared: the linear perceptron and the optimal linear associative memory (OLAM) Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the whole spectrum in the identification process instead of only the individual photo peaks. This approach has been successfully tested with data generated by Monte Carlo simulations and with field data from both sodium iodide and germanium detectors.
author_names: Paul E Keller

Lars J Kangas

G L Troyer

Sherif Hashem

Richard T Kouzes
title: Nuclear spectral analysis via artificial neural networks for waste handling
year: 1995

abstract: Polymers as Drug Delivery Carriers Hydrophilic Cellulose Derivatives as Drug Delivery Carriers: Influence of Substitution Type on the Properties of Compressed Matrix Tablets, Carmen Ferrero Rodriguez, Nathalie Bruneau, Jerome Barra, Dorothee Alfonso, and Eric Doelker Poly(Vinyl Alcohol) as a Drug Delivery Carrier, Surya K. Mallapragada and Shannon McCarthy Schroeder Development of Acrylate and Methacrylate Polymer Networks for Controlled Release by Photopolymerization Technology, Robert Scott, Jennifer H. Ward, and Nicholas A. Peppas Smart Polymers for Controlled Drug Delivery, Joseph Kost and Smadar A. Lapidot Complexing Polymers in Drug Delivery, Anthony M. Lowman Polylactic and Polyglycolic Acids as Drug Delivery Carriers, Lisa Brannon Peppas and Michel Vert Use of Infrared and Raman Spectroscopy for Characterization of Controlled Release Systems, A. B. Scranton, B. Drescher, E. W. Nelson, and J. L. Jacobs Accurate Models in Controlled Drug Delivery Systems, Balaji Narasimhan Mechanism Based Classification of Controlled Release Devices Drug Release from Swelling Controlled Systems, Paolo Colombo, Patrizia Santi, Ruggero Bettini,Christopher S. Brazel, and Nikolaos A. Peppas Superporous Hydrogels as a Platform for Oral Controlled Drug Delivery, Haesun Park, Jun Chen, and Kinam Park Osmotic Implantable Delivery Systems, Jeremy C. Wright, Felix Theeuwes, and Cynthia L. Stevenson Bioadhesive Controlled Release Systems, Nicholas A. Peppas, Monica D. Little, and Yanbin Huang Micro and Nanoparticulate Release Systems Microencapsulation Technology: Interfacial Polymerization Method, A. Atila Hincal and H. Suheyla Kas Nanoparticulate Controlled Release Systems for Cancer Therapy, C. Dubernet, E. Fattal, and P. Couvreur Microencapsulation Using Coacervation/Phase Separation: An Overview of the Technique and Applications, H. Suheyla Kas and Levent Oner Microsphere Preparation by Solvent Evaporation Method, A. Atila Hincal and Sema Calis Nanosuspensions A Formulation Approach for Poorly Soluble and Poorly Bioavailable Drugs, R. H. Muller, B. H. L. Bohm, and M. J. Grau Large Scale Production of Solid Lipid Nanoparticles (SLN) and Nanosuspensions (DissoCubes) R. H. Muller, S. Gohla, A. Dingler, and T. Schneppe Solid Lipid Nanoparticles (SLN) as a Carrier System for the Controlled Release of Drugs, R. H. Muller, A. Lippacher, and S. Gohla Stability of Encapsulated Substances in Poly(Lactide co Glycolide) Delivery Systems, Steven P. Schwendeman, Gaozhong Zhu, Anna Shenderova, and Wenle Jiang Development of Polysaccharides as Novel Drug Carrier Systems, C. Vauthier and P. Couvreur Classification of Controlled Release Devices According to Administration Site An Overview of Controlled Release Systems, S. Venkatraman, N. Davar, A. Chester, and L. Kleiner Research and Development Aspects of Oral Controlled Release Dosage Forms, Yihong Qiu and Guohua Zhang A Gastrointestinal Retentive Microparticulate System to Improve Oral Drug Delivery, Y. Kawashima, H. Takeuchi, and H. Yamamoto In Vitro In Vivo Correlations in the Development of Solid Oral Controlled Release Dosage Forms, Yihong Qiu, Emil E. Samara, and Guoliang Cao Gamma Scintigraphy in the Analysis of the Behavior of Controlled Release Systems, C. G. Wilson and N. Washington Electrically Assisted Transdermal Delivery of Drugs, Ajay K. Banga A Novel Method Based on Artificial Neural Networks for Optimizing Transdermal Drug Delivery Systems, Kozo Takayama and Tsuneji Nagai Transdermal Drug Delivery by Skin Electroporation, Tani Chen, Robert Langer, and James C. Weaver Enhancement of Transdermal Transport Using Ultrasound in Combination with Other Enhancers, Joseph Kost, Samir Mitragotri, and Robert Langer Electrotransport Systems for Transdermal Delivery: A Practical Implementation of Iontophoresis, Erik R. Scott, J. Bradley Phipps, J. Richard Gyory, and Rama V. Padmanabhan Peptide and Protein Release Systems Controlled Release Protein Therapeutics: Effects of Process and Formulation on Stability, Paul A. Burke Solid State Chemical Stability of Peptides and Proteins: Application to Controlled Release Formulations, Elizabeth M. Topp, Yuan Song, Ashley Wilson, Rong Li, Michael J. Hageman, and Richard L. Schowen Growth Factor Release from Biodegradable Hydrogels to Induce Neovascularization, Yoshita Ikada and Yasuhiko Tabata Biopolymers for Release of Interleukin 2 for Treatment of Cancer, Debra J. Trantolo, Joseph D. Gresser, A. Ganiyu Jimoh, Donald L. Wise,and James C. Yang Medical Applications of Drug Delivery Osmotic Drug Delivery from Asymmetric Membrane Film Coated Dosage Forms, Mary Tanya am Ende, Scott M. Herbig, Richard W. Korsmeyer, and Mark B. Chidlaw Controlled Release Pain Management Systems, Vasif Hasirci, Dilek Sendil, Leonidas C. Goudas, Daniel B. Carr, and Donald L. Wise Biodegradable Systems for Long Acting Nestorone, Debra J. Trantolo, Donald L. Wise, A. J. Moo Young, Yung Yueh Hsu, and Joseph D. Gresser Preparation and Evaluation of Buprenorphine Microspheres for Parenteral Administration, William R. Ravis, Yuh Jing Lin, and Ram Murty Prolonged Release of Hydromorphone from a Novel Poly(Lactic co Glycolic) Acid Depot System: Initial In Vitro and In Vivo Observations, Leonidas C. Goudas, Daniel B. Carr, Richard M. Kream, Louis Shuster, William M. Vaughan, Joseph D. Gresser,Donald L. Wise, and Debra J. Trantolo Incorporation of an Active Agent into a Biodegradable Cement: Encapsulation of the Agent as Protection from Chemical Degradation During Cure and Effect on Release Profile, Joseph D. Gresser, Debra J. Trantolo, Pattisapu R. J. Gangadharam, Hisanori X. Nagaoka,Yung Yueh Hsu, and Donald L. Wise The Pharmacoeconomic Value of Controlled Release Dosage Forms, Laura B. Gardner
author_names: Donald L Wise
title: Handbook of Pharmaceutical Controlled Release Technology
year: 2000

abstract: Shield tunnelling method w as used from 1920 in Japan. As the innovation of closed type shield technology around 1970's, the number of shield tunnel constructions in urban area increased rapidly. As a result, more than 4000 shield tunnels have been Abstract The European project "Technology Innovation in Underground Construction" is the biggest single research initiative ever undertaken on the topic. Some innovations that resulted from the project and that are associated w ith some topics of the conference more $20.00 EUR15.00 Add PDF to cart Abstract A new data mining method of Support Vector Machines (SVM) is applied on the classification of rock mass in tunnels. SVM is a novel pow erful leaning method that based on Statistical Learning Theory. SVM can solve small sample learning problems Abstract In the present paper, Data Mining techniques has been applied to evaluate the stability of slopes. For this propose, the R (w w w .r project.org) softw are w as used together w ith a user defined application developed at the University of Minho called RMiner. The Abstract Jet Grouting (JG) technology is currently applied in many geotechnical w orks for improving mechanics properties of soil, mainly soft soils. In many geotechnical structures advance design incorporates the serviceability design criteria. For this more Abstract This paper introduced the method and principle of a traditional probability neural netw ork (PNN) and an adaptive probability neural netw ork (APNN) Based on inverse problem theory, the question of soil classify is investigated. A new method based on
author_names: Yasushi Arai

Yoshio Nishida

Koji Kayukaw

Wataru Sato

Minoru Kuriki

Vector Machines

Tian-bin Li
title: An Intelligent Rock Mass Classification Method based on Support
year: 2014