The report will be devoted to the modelling of damage processes of the optical-cryogenic sensor at microscopic and
macroscopic levels. The sensor is based on a new type of suspension of the probe of a supeconducting gravimeter.
The interferometric method is provided coordinate measurement of the probe. The following main subjects will
be covered by the report: (1) modelling of a supeconducting gravimeter; (2) modeling of a solid-state laser;
(3) computer simulation of damage processes at microscopic and macroscopic levels; (4) response of thin films
to intense short-wavelength radiation; (5) mathematical models for dynamic probabilistic risk assessment; (6)
strategies for the design of optical components, and (7) software for modeling and prediction of ionizing radiation.
For computer simulation of damage processes at microscopic and macroscopic levels the following methods are
used: () statistical; (b) dynamical; (c) optimization; (d) acceleration modeling, and (e) mathematical modeling
of laser functioning. Mathematical models of space ionizing radiation influence on gravimwter elements were
developed for risk assessment in laser safety analysis.
This report concentrates on dynamic probabilistic risk analysis of optical elements for complex characterization
of damages using physical model of solid state lasers and predictable level of ionizing radiation and space weather.
The following main subjects will be covered by our report: (a) a solid-state laser model; (b) mathematical models
for dynamic probabilistic risk assessment; and (c) software for modeling and prediction of ionizing radiation. A
probabilistic risk assessment method for solid-state lasers is presented with consideration of some deterministic
and stochastic factors. Probabilistic risk assessment is a comprehensive, structured, and logical analysis method
aimed at identifying and assessing risks in solid-state lasers for the purpose of cost-effectively improving their
safety and performance. This method is based on the Conditional Value-at-Risk measure (CVaR) and the
expected loss exceeding Value-at-Risk (VaR). We propose a new dynamical-information approach for radiation
damage risk assessment of laser elements by cosmic radiation. Our approach includes the following steps: (a)laser
modeling, modeling of ionizing radiation influences on laser elements, (b) probabilistic risk assessment methods,
and (c) risk minimization. For computer simulation of damage processes at microscopic and macroscopic levels
the following methods are used: (a) statistical; (b) dynamical; (c) optimization; (d) acceleration modeling, and
(e) mathematical modeling of laser functioning. Mathematical models of space ionizing radiation influence on
laser elements were developed for risk assessment in laser safety analysis. This is a so-called 'black box' or
'input-output' model, which seeks only to reproduce the behaviour of the system's output in response to changes
in its inputs. The model inputs are radiation influences on laser systems and output parameters are dynamical
characteristics of the solid laser.
This paper deals with the progress made in applications of quantum
computing in control and optimization. It concentrates on applying
the geometric technique in order to investigate a finite control
problem of a two-level quantum system, resonance control of a
three-level system, simulation of bilinear quantum control systems,
and optimal control using the Bellman principle. We show that a
quantum object described by a Schroedinger equation can be
controlled in an optimal way by electromagnetic modes. We also
demonstrate an application of these techniques and an
algebra-geometric approach to the study of dynamic processes in
nonlinear systems. The information processing by means of controlled
quantum lattices is discussed: we present new mathematical models of
classical (CL) and quantum-mechanical lattices (QML) and their
application to information processing. system-theoretical results on
the observability, controllability and minimal realizability
theorems are formulated for cl. The cellular dynamaton (CD) based on
quantum oscillators is presented. Cellular's quantum computational
search procedure can provide the basis for implementing adaptive
global optimization algorithms. A brief overview of the procedure is
given and a framework called lattice adaptive search is set up. A
method of Yatsenko and one introduced by the authors fit into this
framework and are compared.
Vegetation is a sensitive indicator suitable for testing of ecological stresses and natural anomalies of the technogenic character. First, it is determined by the prompt response of photosynthetic apparatus to changes of environmental conditions, mainly by change of green pigment (chlorophyll) content in leaves. Second, the specific kind of a reflectance spectrum of leaves is due to chlorophyll presence in them, and the area in the range of 500-80 nm is extremely sensitive to variations of its pigment content. Thirdly, there are interesting results now concerning spectral properties of leaves and crops canopies obtaining with high-resolution spectroscopy. The data are high informative in relation to content of chlorophyll and some other biochemical constituents of a cell. The high resistance to various types of noises is inherent to methods developed on the basis of such spectral data. We have developed a method for chlorophyll estimation using the 1-st derivative plots of reflectance spectral curves. The method gives good results for plant-soil systems with both for 100% and
incomplete projective covering as our simulation models show. Field measurements of chlorophyll content in closed and open canopies crops confirm the results. A hardware-software complex has been produced by us for chlorophyll determining under field conditions. It consists of spectral and computing blocks. First of them is a two-beam spectrometer of high resolution supplied by a system to visualize of measured object. The irradiance and temperature sensors are included to the spectral block as well as GPS-receiver. The following technical characteristics are inherent to the block: spectral range 500-800 nm, band-pass 1.5 nm, field of view 16x16o, scanning time 0.1-1.0 s, dynamic range of signal 1:1024 (10 bit), signal/noise ratio 400, amount of pixels in image 1240, range of estimated chlorophyll concentrations 1.5-8.0 mg/dm2, supply voltage 12 V, weight 8 kg. Computing block is intended for spectral date processing to obtain chlorophyll estimations using our algorithm. The block is supplied by our original software WINCHL, which includes spectrum and algorithm
libraries and various mathematical tools. Accumulation of reflectance spectra of various plants together with data of environmental conditions at measurements gives a good possibility to use all of them for future scientific researches and developing other important parameters of canopy status.
This paper describes a new approach to global optimization and
control uses geometric methods and modern quantum mathematics.
Polynomial extremal problems (PEP) are considered. PEP
constitute one of the most important subclasses of nonlinear
programming models. Their distinctive feature is that an objective
function and constraints can be expressed by polynomial functions
in one or several variables. A general approach to optimization
based on quantum holonomic computing algorithms and instanton
mechanism. An optimization method based on geometric Lie -
algebraic structures on Grassmann manifolds and related with Lax
type flows is proposed. Making use of the differential geometric
techniques it is shown that associated holonomy groups properly
realizing quantum computation can be effectively found concerning
polynomial problems. Two examples demonstrating calculation
aspects of holonomic quantum computer and maximum clique problems
in very large graphs,
are considered in detail.
It is known that leaf reflectance spectra can be used to estimate the contents of chemical components in vegetation. Recent novel applications include the detection of harmful biological agents that can originate from agricultural bioterrorism attacks. Such attacks have been identified as a major threat to the United States’ agriculture. Nevertheless, the usefulness of such approach is currently limited by distorting factors, in particular soil reflectance.
The quantitative analysis of the spectral curves from the reflection of plant leaves may be the basis for the development of new methods for interpreting the data obtained by the remote measurement of plants. We consider the problem of characterizing the chemical composition from noisy spectral data using an experimental optical method.
Using our experience in signal processing and optimization of complex systems we propose a new mathematical model for sensing of chemical components in vegetation. Estimates are defined as minimizers of penalized cost functionals with sequential quadratic programming (SQR) methods. A deviation measure used in risk analysis is also considered.
This framework is demonstrated for different agricultural plants using adaptive filtration, principal components analysis, and optimization techniques for classification of spectral curves of chemical components. Various estimation problems will be considered to illustrate the computational aspects of the proposed method.
It is shown that the spectral curve of reflectance of vegetation
contains the sufficient information to create a set of parameters
for effective monitoring of agricultural crops. Most of them are
based on the chlorophyll estimation or characteristics, which are
dependent on specific influence of inner structure of plant
tissues on leaf reflection in the region of chlorophyll
absorption. New chlorophyll indices are proposed for estimation of
chlorophyll content in leaves using the shape of leaf reflectance
curves. The ratio of two maxima in the 1-st derivative plot from
reflectance spectral curve in 680-750 nm region has been shown
to correlate with chlorophyll content in winter wheat leaves.
Independent component analysis of reflectance spectral curves has
been applied as well. An interrelation between the chlorophyll
concentration and vectors of principal components has been found.
The estimates of the chlorophyll content by using of these
parameters and regression equations gave suitable results.
Comparison of two approaches has been performed. Stability of both
approaches with regard to incomplete project covering have been
tested. Usage of physical and graphical models permits to estimate
stability in calculation results of chlorophyll concentration
influence of soil reflection. It has been shown that the ratio of
two maxima in the 1-st derivative plot was changed now more than
5% and 11% under 50 % and 25 % projective cover,
respectively, on a background of dark soil or sand. The
reflectance coefficient at 550 nm correlates with chlorophyll
content but it is highly sensitive to contribution of soil
reflectance. Therefore combination of chlorophyll estimates
obtained by red edge parameters and the reflectance coefficient at
550 nm gives possibility to estimate a projective covering. We
shown that principal components approach is resistant to
influence of project covering.
Today, remote sensing is one of the fastest growing technologies around. It is a multibillion-dollar industry and remote thematic images are routinely used in an increasing number of fields. The solution of many important practical problems depends on a large-scale usage of the measurement systems and underlying physical principles. These problems include monitoring of the natural resources based on the analysis of the gravity anomalies, studying of global geodynamic processes and evolution of the Earth gravity field, analysis of movement of the Earth poles, etc. In spite of the existence of the considerable achievements in the area of gravity measurements, some important aspects of the problem have not been solved yet due to the absence of appropriate sensitive elements (SE) and sensors with the relevant parameters. The author of the report has proposed a functional structure of the cryogenic-optical sensor based on magnetic bearing phenomenon. A functional structure of the sensitive element consists of a controlled magnetic suspension, a high-precision optical system for registration of levitating body mechanical coordinates, and a signal processing toolbox. This toolbox contents the adaptive compensator, digital filters, inverse mathematical models of the SE, the Kalman filter, the control system, the dynamical analysis system, the mathematical modeling system, the simulation system, the information statistical system, the wavelet analysis system, a neural network, and data base. Mathematical models of the signal and noise are conventionally based on the principles of nonlinear electro-mechanics. Such models explains most basic features of the superconducting sensitive element. We will also discuss a new theoretical framework for adaptive estimation of gravitation perturbations and compare program models to conventional robust estimation models.
Using our experience in signal processing and optimization of complex systems we propose a new method to adaptive sensing of chemical content of vegetations. This framework is demonstrated for different agricultural plants using the neural network algorithm for classification of spectral curves and adaptive filtration. Utilization of characteristics of leaf reflectance spectrum, which are a relative characteristic of the light reflected from canopies, makes it possible to avoid the necessity of measuring the 100% reflectance standard and to provide the high resistance of the method to distorting factors in particular to soil reflectance contribution. For utilization of the method the numerical algorithms is proposed. Various estimation problems will be considered to illustrate the computational aspects of the proposed method. The software is based on digital filter, optimization approach and neural network algorithm for classification of chemical components. Supporting software for data management, storage, signal processing will be development. A concept of an intelligent sensor is considered.
In this paper we consider the problem of estimating chlorophyll content in vegetation using an experimental optical method from noisy spectral data. It is shown that the quantitative analysis of the spectral curves for the reflection of plant leaves may be the basis for development of new methods for interpretation of the data obtained by the remote measurement of plants. A mathematical model of vegetation reflectance is proposed to estimate the chlorophyll content from spectral data. Estimates are defined as minimizers of penalized cost functionals with sequential quadratic programming (SQR) methods. An estimation is related to the local scoring procedure for the generalized additive model. A deviation measurement in risk analysis of vegetation is considered. The role of deviation and risk measures in optimization is analyzed. Experimental and simulation results are shown for different agricultural plants using a functional-parametric representation of spectral curves.
This article discusses the principle and performance of a controlled sensitive element. A concept of the cryogenicoptical sensor based on competitive the adaptive sensitive element applicable to a gravity meter sensor is considered. The sensor element is based on a magnetic levitation phenomenon, high-precision optical registration of levitating body mechanical coordinates, and robust signal processing tools. A controlled self-bearing probe dynamics is also analyzed. An dynamical approach to highly sensitive measurement of weak signal is presented. The robust signal estimation problem is considered, when signal are estimated via application of neural networks and when nonlinear measurements are used. The construction of the sensor is described. Simulation results support the mathematical, and the system characteristics are thus optimized.
This paper presents a quantum optimization problem and
solid-state quantum computing architectures. Quantum approach to
global optimization and NP-complete problems are considered. Our
approach to global optimization based on quantum mechanical
entanglement, quantum resonant tunneling, cellular automaton and
geometric control methods. A quantum optimization algorithm
combines the properties of classical simulated annealing with the
possibility of quantum tunneling between the minima. Quantum
computation exploits the property of quantum states to implement
quantum parallelism for global nonconvex optimization problem.
This paper considers new mathematical models of classical (CL)
and quantum-mechanical lattices (QML). System-theoretic results
on the observability, controllability and minimal realizability
theorems are formulated for CL. The cellular dynamaton (CD) based
on quantum oscillators is presented. We investigate the conditions
when stochastic resonance can occur through the interaction of
dynamical neurons with intrinsic deterministic noise and an
external periodic control.
A concept of the cryogenic-optical sensor based on competitive
adaptive sensitive elements applicable to a gravity meter sensor
is considered. The sensor element is based on a magnetic
levitation phenomenon, high-precision optical registration of
levitating body mechanical coordinates, and robust signal
processing tools. A controlled self-bearing probe dynamics is
analyzed. An optimization approach to highly sensitive
measurement of weak signal is presented. An optimization method
which allows the extraction of the Lyapunov exponents from
nonlinear chaotic dynamics of a macroscopic superconducting probe
is described. Simulation results support the mathematical, and the
system characteristics are thus optimized.
In this paper we consider the problem of estimating chlorophyll content in vegetation using an experimental optical method from noisy spectral data. It is shown that the quantitative analysis of the spectral curves for the reflection of plant leaves may be the basis for development of new methods for interpretation of the data obtained by the remote measurement of plants. A mathematical model of vegetation reflectance is proposed to estimate the chlorophyll concentration from spectral data. Estimates are defined as minimizers of penalized cost functionals with sequential quadratic programming (SQP) methods. An estimation tool is related to the local scoring procedure for an generalized additive model. Experimental and simulation results are shown for different agricultural plants using a functional parametric fitting of spectral curves.
We describe a phenomenon in which a macroscopic superconducting probe, as large as 2 - 6 cm, is chaotically and magnetically levitated. We have found that, when feedback is used, the probe chaotically moves near an equilibrium state. The global optimization approach to highly sensitive measurement of weak signal is considered. Furthermore an accurate mathematical model of asymptotically stable estimation of a limiting weak noisy signal using the stochastic measurement model is considered.
In this paper we have researched the influence of pollutants made on such biological objects as photosynthesizing systems in order to reveal the capabilities and features of their application as the controlled sensor in integral ecological monitoring systems.
The report contains the results of the investigation of intelligent sensor dynamics. The basis here is the nonlinear controlled processes running in the reaction centers of purple bacteria and in Langmuir-Blodgett films. The quantum processes are taken into consideration, and this report theoretically explains how pollution affects the optical properties of the reaction centers. The transportation of charges is also examined. This paper presents the equation describing transformation of the water pollution data into an electrical signal convenient for processing in the cases when the adaptive Kalman filter or the neural network based on the dynamical elements are used. The report analyzes the robust characteristics of the sensor on the basis of Kalman filter. The water analysis system efficiency can be improved using the dual measurement principle suggesting identification of a biosensor model according to experimental data.
An intelligent sensor is presented, which is addressed for application in ecological monitoring. This intelligent biosensor is based on the probabilistic small-size neurochip and Langmuir- Blodgett film and its is used to detect the ecological state of industrial waters. The new concept of intelligent sensor self-organization is discussed in connection with the soliton waves moving inside Langmuir-Blodgett films.
We consider the use of some superconductivity effects for estimating the vibration signal picked up by a probe in a controlled potential well. Sensitivity in excess of previously attained levels is achieved.
The paper proposes the new investigation results as for creation of the contemporary neural sensors functioning in soil, water, and atmosphere. The structures possessed by a polyfunctional ecologic sensor, an optical/chemical nonreacting sensor, a polyfunctional laser sensor and a highly sensitive gravi-inertial sensor. As for the case with intelligent sensors, then the probabilistic small-size neurochips and Langmuir-Blodgett films may be used. This system is robust and it is aimed at the eco-objects which possess the undetermined parameters. The input/output sensor date are analyzed. The dynamic properties inherent in the above- mentioned sensors are ivestigated. The support development results are given.