scholarly journals INVERSE PROBLEMS OF SOIL BIOKINETICS

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mikhail Vladimirovich Glagolev
Keyword(s):  
Inverse Problems ◽  
Soil Column ◽  
Concentration Field ◽  
Soil Science ◽  
Layer By Layer ◽  
State University ◽  
Gas Source ◽  
Ill Posed ◽  
Biological Kinetics

This work represents the materials of the report prepared at the suggestion of N. S. Panikov in 19851986, when the author was a third-year student at the Faculty of Soil Science, M.V. Lomonosov Moscow State University. The report contains definitions of direct and inverse problems. A classification of inverse problems and several examples of such problems encountered in soil science and biological kinetics are given. The question of the ill-posed inverse problems is touched, and the main methods of their solution are briefly listed. The problem of identifying a gas source in a soil column by the layer-by-layer balance method (based on measurements of the dynamics of the concentration field) is considered in detail. This task is shown as a computer program, and for others, useful links to programs published in the literature are given.

scholarly journals International conference «Soil as interlink for functioning of natural and anthropogenically transformed ecosystems» devoted to the 90th anniversary of the department of pedology and land resources estimation, Irkutsk State University and year of Baikal

2021 ◽  
Vol 4 (3) ◽  
pp. 1-26
Author(s):  
T. V. Nechaeva ◽  
N. A. Sokolova ◽  
N. D. Kiseleva
Keyword(s):  
Lake Baikal ◽  
Classification Systems ◽  
Soil Science ◽  
State University ◽  
Land Resources ◽  
Wide Range ◽  
90Th Anniversary ◽  
The Village ◽  
Bratsk Reservoir

From August 23 to August 29, 2021, Irkutsk hosted the Vth International Scientific and Practical Conference "Soil as interlink for functioning of natural and anthropogenically transformed ecosystems" (hereinafter – the conference) dedicated to the 90th anniversary of the Department of Soil Science and Land Resources Assessment of the Irkutsk State University (ISU) and the Year of Baikal. The total number of participants of the conference was 130 from 27 regions of Russia and 6 other countries (the Republic of Belarus, Bulgaria, Georgia, Moldova, Lebanon and Lithuania). The article presents a brief review of plenary and sectional reports on the following research topics: 1) theoretical soil science: genesis, evolution, classification problems; 2). multidisciplinary approaches of soil science related to the use of soil science methods in other research areas and scientific and industrial areas; 3) Soil resources and land assessment (fertility, degradation, land reclamation, qualitative and economic assessment, ecology and land protection). A total of 43 presentations were given at the conference: 8 plenary and 35 sectional. The interested reader will find a detailed description of the reports presented in this review, as well as other and other conference materials in the collection "Soil as interlink for functioning of natural and anthropogenically transformed ecosystems" (2021). A brief description of two excursions is given: (1) one excursion to the beautiful scenery at the shore of the Lake Baikal, held on August 23, 2021; (2) and another excursion at the Bratsk Reservoir on August 26-29, 2021. The purpose of the excursions wass to get acquainted with the nature and historical and cultural heritage of the Irkutsk region, Lake Baikal, as well as with soils, soil-forming rocks and natural features of the Southern Angara region. During the excursion tour, landscapes, rock outcrops and soil sections were presented: soils on a bumpy-depression relief; Paleolithic site of ancient man "Malta" with sections near the geoarchaeological objects "Malta-Bridge 3"; alluvial gray-humus soil in the floodplain of the Belaya River; exposure of Lower Cambrian rocks near the village Novomaltinsk; Cheremkhovsky coal mine; dispersed-carbonate gypseous chernozem near the Unga River; Novonukutsky gypsum mine; gray metamorphic soil and micellar-segregational chernozem on the bank of the Bratsk reservoir near the village Balagansk. At the end of the tour, the conference participants held a roundtable discussion about the problems of genesis and classification of the soils of the south of the Near-Angara region. The classification position of all the presented soils was justified within the framework of two classification systems: Classification of soils of Russia (2004) and Classification and diagnostics of soils of the USSR (1977). For scientific and informational support of the excursion, the guide "Southern Pre-Angara region: features of soil formation on rocks of different ages" (2021) was prepared and published. The conference aroused great interest among a wide range of specialists in the field of soil science, agrochemistry and ecology, land resource assessment, landscape studies, etc. The organization of such events promotes exchange of experience and strengthens the cooperation between researchers from leading universities and research centers, advancing the effective development of soil science, research methodology and practice, generalizing the information about soil as a link between the functioning of natural and anthropogenically transformed ecosystems.

Methods of solving ill-posed inverse problems

1983 ◽  
Vol 45 (5) ◽  
pp. 1237-1245 ◽  
Author(s):  
O. M. Alifanov
Keyword(s):  
Inverse Problems ◽  
Ill Posed

scholarly journals Expert-level classification of gastritis by endoscopy using deep learning: a multicenter diagnostic trial

2021 ◽  
Vol 09 (06) ◽  
pp. E955-E964
Author(s):  
Ganggang Mu ◽  
Yijie Zhu ◽  
Zhanyue Niu ◽  
Shigang Ding ◽  
Honggang Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Atrophic Gastritis ◽  
Intestinal Metaplasia ◽  
Layer By Layer ◽  
Test Set ◽  
White Light Endoscopy ◽  
High Risk Factors ◽  
External Test ◽  
Diagnostic Trial

Abstract Background and study aims Endoscopy plays a crucial role in diagnosis of gastritis. Endoscopists have low accuracy in diagnosing atrophic gastritis with white-light endoscopy (WLE). High-risk factors (such as atrophic gastritis [AG]) for carcinogenesis demand early detection. Deep learning (DL)-based gastritis classification with WLE rarely has been reported. We built a system for improving the accuracy of diagnosis of AG with WLE to assist with this common gastritis diagnosis and help lessen endoscopist fatigue. Methods We collected a total of 8141 endoscopic images of common gastritis, other gastritis, and non-gastritis in 4587 cases and built a DL -based system constructed with UNet + + and Resnet-50. A system was developed to sort common gastritis images layer by layer: The first layer included non-gastritis/common gastritis/other gastritis, the second layer contained AG/non-atrophic gastritis, and the third layer included atrophy/intestinal metaplasia and erosion/hemorrhage. The convolutional neural networks were tested with three separate test sets. Results Rates of accuracy for classifying non-atrophic gastritis/AG, atrophy/intestinal metaplasia, and erosion/hemorrhage were 88.78 %, 87.40 %, and 93.67 % in internal test set, 91.23 %, 85.81 %, and 92.70 % in the external test set ,and 95.00 %, 92.86 %, and 94.74 % in the video set, respectively. The hit ratio with the segmentation model was 99.29 %. The accuracy for detection of non-gastritis/common gastritis/other gastritis was 93.6 %. Conclusions The system had decent specificity and accuracy in classification of gastritis lesions. DL has great potential in WLE gastritis classification for assisting with achieving accurate diagnoses after endoscopic procedures.

Regularization of Some One-Dimensional Inverse Problems for Identification of Nonlinear Surface Phenomena

1995 ◽  
Author(s):  
C. W. Groetsch ◽  
Martin Hanke
Keyword(s):  
Inverse Problems ◽  
Model Identification ◽  
Surface Phenomena ◽  
Unbounded Operators ◽  
General Technique ◽  
Regularization Technique ◽  
One Dimensional ◽  
Data Errors ◽  
Ill Posed ◽  
Nonlinear Surface

Abstract A simple numerical method for some one-dimensional inverse problems of model identification type arising in nonlinear heat transfer is discussed. The essence of the method is to express the nonlinearity in terms of an integro-differential operator, the values of which are approximated by a linear spline technique. The inverse problems are mildly ill-posed and therefore call for regularization when data errors are present. A general technique for stabilization of unbounded operators may be applied to regularize the process and a specific regularization technique is illustrated on a model problem.

Hierarchical and Multi-resolution Neuron Network Computing for Complex Inverse Problems-An Approach Based on Brain-inspired Computing and Learning Method

2021 ◽  
Vol 01 ◽  
Author(s):  
Mingyong Zhou
Keyword(s):  
Deep Learning ◽  
Inverse Problems ◽  
Radar Imaging ◽  
Network Computing ◽  
Neuron Network ◽  
Impedance Tomography ◽  
General Complex ◽  
Mathematical Algorithms ◽  
Ill Posed ◽  
Regular Operators

Background: Complex inverse problems such as Radar Imaging and CT/EIT imaging are well investigated in mathematical algorithms with various regularization methods. However it is difficult to obtain stable inverse solutions with fast convergence and high accuracy at the same time due to the ill-posed property and non-linear property. Objective: In this paper, we propose a hierarchical and multi-resolution scalable method from both algorithm perspective and hardware perspective to achieve fast and accurate solu-tions for inverse problems by taking radar and EIT imaging as examples. Method: We present an extension of discussion on neuromorphic computing as brain-inspired computing method and the learning/training algorithm to design a series of problem specific AI “brains” (with different memristive values) to solve a general complex ill-posed inverse problems that are traditionally solved by mathematical regular operators. We design a hierarchical and multi-resolution scalable method and an algorithm framework to train AI deep learning neuron network and map into the memristive circuit so that the memristive val-ues are optimally obtained. We propose FPGA as an emulation implementation for neuro-morphic circuit as well. Result: We compared the methodology between our approach and traditional regulariza-tion method. In particular we use Electrical Impedance Tomography (EIT) and Radar imaging as typical examples to compare how to design an AI deep learning neuron network architec-tures to solve inverse problems. Conclusion: With EIT imaging as a typical example, we show that any moderate complex inverse problem, as long as it can be described as combinational problem, AI deep learning neuron network is a practical alternative approach to try to solve the inverse problems with any given expected resolution accuracy, as long as the neuron network width is large enough and computational power is strong enough for all combination samples training purpose.

scholarly journals The Regularized Weak Functional Matching Pursuit for linear inverse problems

2019 ◽  
Vol 27 (3) ◽  
pp. 317-340 ◽  
Author(s):  
Max Kontak ◽  
Volker Michel
Keyword(s):  
Inverse Problems ◽  
Matching Pursuit ◽  
A Priori ◽  
Computation Time ◽  
Point Of View ◽  
Computational Point ◽  
Linear Inverse Problems ◽  
Infinite Dimensional ◽  
Frame Condition ◽  
Ill Posed

Abstract In this work, we present the so-called Regularized Weak Functional Matching Pursuit (RWFMP) algorithm, which is a weak greedy algorithm for linear ill-posed inverse problems. In comparison to the Regularized Functional Matching Pursuit (RFMP), on which it is based, the RWFMP possesses an improved theoretical analysis including the guaranteed existence of the iterates, the convergence of the algorithm for inverse problems in infinite-dimensional Hilbert spaces, and a convergence rate, which is also valid for the particular case of the RFMP. Another improvement is the cancellation of the previously required and difficult to verify semi-frame condition. Furthermore, we provide an a-priori parameter choice rule for the RWFMP, which yields a convergent regularization. Finally, we will give a numerical example, which shows that the “weak” approach is also beneficial from the computational point of view. By applying an improved search strategy in the algorithm, which is motivated by the weak approach, we can save up to 90  of computation time in comparison to the RFMP, whereas the accuracy of the solution does not change as much.

scholarly journals Towards Odor-Sensitive Mobile Robots

2018 ◽  
pp. 244-263 ◽  
Author(s):  
Javier Monroy ◽  
Javier Gonzalez-Jimenez
Keyword(s):  
Mobile Robots ◽  
Gas Sensing ◽  
Laser Scanner ◽  
Sensory Information ◽  
Current Status ◽  
Electronic Noses ◽  
Gas Dispersion ◽  
Gas Source ◽  
Sparse Measurements

Out of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment.

scholarly journals Optimization Learning: Perspective, Method, and Applications

2020 ◽  
Author(s):  
Risheng Liu
Keyword(s):  
Inverse Problems ◽  
Theoretical Analysis ◽  
Iterative Methods ◽  
State Of The Art ◽  
Learning Paradigm ◽  
Rigorous Analysis ◽  
The Core ◽  
Globally Convergent ◽  
Ill Posed ◽  
Art Performance

Numerous tasks at the core of statistics, learning, and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis of the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. We move beyond these limits and propose a theoretically guaranteed optimization learning paradigm, a generic and provable paradigm for nonconvex inverse problems, and develop a series of convergent deep models. Our theoretical analysis reveals that the proposed optimization learning paradigm allows us to generate globally convergent trajectories for learning-based iterative methods. Thanks to the superiority of our framework, we achieve state-of-the-art performance on different real applications.

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