Improvement of the Algorithm of Algebraic Constructive Logic Model

2015 ◽  
Vol 22 (2) ◽  
pp. 11-19 ◽  
Author(s):  
Хромушин ◽  
Oleg Khromushin ◽  
Хромушин ◽  
Viktor Khromushin ◽  
Китанина ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Multivariate Analysis ◽  
Final Stage ◽  
Detection Limits ◽  
Algebraic Model ◽  
Logic Model ◽  
Constructive Logic ◽  
Partial Overlap ◽  
The Mathematical Model ◽  
Better Than

For many years, algebraic constructive logic model is used for multivariate analysis in medicine and biology. The classic version of this model includes the exclusion of contradictory accounts, i.e. when the target is achieved and not achieved in the presence of the same values of the factors. In this case, the lines as appropriate to achieving target, and its failure are removed, including significant proportions. Another feature of this algorithm is the partial overlap of the intervals to determine the factors resulting in components in achieving a target and not achieving despite the exclusion of contradictory accounts. The authors explain this by the fact that the classical algorithm generates the detection limits of the factors in resulting components with some capture values that are related to the lines of not achieving the target (up to inappropriate values). To some extent this reduces the accuracy of the mathematical model. A further feature of the algorithm is the necessary to optimize mathematical model by excluding re-coating lines. This is acceptable, but not optimal. This requires additional procedures at the final stage of formation of the mathematical model. The proposed version of the algebraic model of constructive logic allows to eliminating the above drawbacks. This is achieved the measure of approximation and a way of combining the cases in the resulting components. The proposed algorithm was tested using specially designed software that allows to exclude controversial cases and to form a mathematical model. Testing showed that the proposed algorithm is better than the classic version and meets the objectives of multivariate analysis in medicine and biology.

Identification Method of Restrictions of Treatment Method Using Algebraic Model of Constructive Logic on the Example of Hyperbaric Oxygen Therapy in Oncological Pathology

2015 ◽  
Vol 22 (3) ◽  
pp. 79-86 ◽  
Author(s):  
Дзасохов ◽  
Aleksey Dzasokhov ◽  
Хромушин ◽  
Viktor Khromushin ◽  
Китанина ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Hyperbaric Oxygen ◽  
Hyperbaric Oxygen Therapy ◽  
Oxygen Therapy ◽  
Algebraic Model ◽  
Total Power ◽  
Constructive Logic ◽  
Number Of Patients ◽  
The Mathematical Model ◽  
Oncological Pathology

Mathematical device of algebraic model of constructive logic has been used for many years for multivariate analysis in medicine and biology most often to identify causal relationships. This mathematical apparatus can be used for more complex analysis schemes for the purpose of determining the contingent of patients who require this method of treatment. The proposed method is a two-step analysis using algebraic model of constructive logic with different specified purposes and subsequent analysis of the resulting final components of the mathematical model. As a result, it is possible to identify restrictions and to quantify the number of patients who need to analyzed method of treatment. The proposed method is explained by an analytical study of hyperbaric oxygen therapy in oncological pathology. Analysis of the results revealed 7,87-39,35% of patients requiring hyperbaric oxygen therapy. The authors revealed the restrictions presented resulting final components of the mathematical model in the form of limits of detection of the combined factors. The equity analysis of values of the resulting components of the mathematical model is associated with the need to calculate the maximum possible total power of the resulting components of the mathematical model, used in expert systems.

Optimization of the Algebraic Model of Constructive Logic

10.12737/2691 ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Хромушин ◽  
Oleg Khromushin ◽  
Хромушин ◽  
Viktor Khromushin ◽  
Дзасохов ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Oxygen Therapy ◽  
Model Identification ◽  
Algebraic Model ◽  
Constructive Logic ◽  
Top Down ◽  
Number Of Components ◽  
Depth Analysis ◽  
The Mathematical Model ◽  
Oncological Pathology

The authors proposed and evaluated options of optimization of the algebraic model of constructive logic, designed to construct multichannel non-linear mathematical model often used in Russia in the in-depth analysis in medicine and biology. In the basis of optimization of this model are procedures for finding duplicate cases (rows base), relevant to the achievement of goals, and excluding those resulting components that are duplicated other cases the resulting components. Procedures for reviewing the results of the components of a top-down or bottom-up and comparing the numbers corresponding to achievement of objectives are the basis of optimization. If all the numbers viewing the resulting component will be present in other watched the resulting components, then it is removed as redundant. As a result of identifying and eliminating redundant coatings target lines are reducing the number of resulting parts. Reduction of number of resulting components is achieved by identifying and eliminating redundant coatings target lines. The results of two variants of optimization of mathematical model are shown on the example of the mathematical model identification features of the method of oxygen therapy in the treatment of oncological pathology. The authors suggested the possibility of practical use of various optimization algorithms to choose model with a minimal number of components of the resulting/

The programm for highlighting the main components resulting in the algebraic model of constructive logic

10.12737/5612 ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Хромушин ◽  
Viktor Khromushin ◽  
Хромушин ◽  
Oleg Khromushin
Keyword(s):  
Mathematical Model ◽  
Inflection Point ◽  
Detection Limits ◽  
Algebraic Model ◽  
Constructive Logic ◽  
Straight Line ◽  
Nonlinear Mathematical Models ◽  
Main Components ◽  
Combined Mark

The article presents the program to determine the principal components resulting in the algebraic model of constructive logic, which is designed for construction multivariate nonlinear mathematical models. The resulting mathematical model is represented by a set of resulting components as factors indicating the detection limits, combined mark of conjunction (indicating joint impact). Each resulting component is characterized by power, which is the essence of the number of rows in the table that match the specified detection limits factors in their joint action. The program provides two methods to determine the main result components. The first method is based on determining the minimum difference between increasing amounts of capacity resulting components of the top and bottom. The second method is based on the determination of the inflection point of the curve decreasing capacity of the resulting components. The authors give recommendations on the choice of allocation method the main result components. If the curve changes power has a dedicated point of inflection and more like a straight line, it is recommended to use method 1. If the curve changes power has a dedicated point of inflection, it is recommended to use method 2. The program should be used in the package of analytical programs algebraic model of constructive logic when performing complex analytical calculations in biophysics, medicine and biology.

Formation of analytical data for multivariate analysis using algebraic model of constructive logic

2015 ◽  
pp. 0-0 ◽  
Author(s):  
S. Fedorov ◽  
K. Kitanina ◽  
V. Khromushin ◽  
O. Khromushin
Keyword(s):  
Mathematical Model ◽  
Multivariate Analysis ◽  
Analytical Data ◽  
Algebraic Model ◽  
Constructive Logic ◽  
Optimum Yield ◽  
Joint Influence ◽  
Source Data ◽  
Age Cohort ◽  
Input Table

Mathematical device of algebraic model of constructive logic has been used for many years for multivariate analysis in medicine and biology. The resulting mathematical model is represented by a set of output components in the form of factors indicating the detection restrictions, which are united by the sign of con-junction (indicating joint influence). Each resulting component is characterized by a capacity, which is the es-sence of the number of rows in the table with the same factors and their intervals of definition. These capacities characterize the degree of influence of the resulting component on the overall result. The input table must not contain contradictions (when the goal is achieved and not achieved when the same values of the factors). For this purpose, the computer program provides for an exception to those target lines, which coincide with non-target rows. However, this is not always acceptable in cases of a large number of matching target lines and unit numbers of non-target rows or vice versa, because a large number of cases is excluded because of a single non-target rows or single target lines. These contradictions arise, primarily, due to the probabilistic nature of the cases. This is clearly seen in the monitoring of mortality. In this article the authors propose three ways optimum yield conflicting source data, based on the excess multiplicity of frequencies matching target and non-target cases and estimates of confidence intervals. The pro-posed methods are examined by analyzing data on deaths of persons aged 18 years and older, residents of the Tula region for 2007-2014 (total 208269 cases). An age cohort 45-54 years is a goal of study. The application of methods of optimum yield conflicting source data is a necessity, which not only im-proves the mathematical model, but, in some cases, is the only way to perform multivariate analysis. All pro-posed methods have their own scope of use, depending on the circumstances.

The target orientation of the multivariate analysis using algebraic model of constructive logic

2015 ◽  
pp. 0-0 ◽  
Author(s):  
S. Fedorov ◽  
K. Kitanina ◽  
V. Khromushin ◽  
O. Khromushin
Keyword(s):  
Mathematical Model ◽  
Multivariate Analysis ◽  
Threshold Value ◽  
Algebraic Model ◽  
Biological Research ◽  
Constructive Logic ◽  
Source Information ◽  
Target Value ◽  
A Value ◽  
Target Values

Multivariate analysis, including algebraic model of constructive logic, is often used in medical practice and biological research. To carry out such studies, it is necessary a array of source information (analyzed cases) and purpose, which is most often selected one of the values of the factors. At the same time, in the practice of analytical calculations there are cases when the target value cannot be set explicitly. The aim of this work is to provide a method of calculating target values for specific cases of morbidity and mortality. The proposed method is based on counting the number of instances of each value of each factor and their share in the total number of cases. The product of the assessed values of each involved factor, compared with the set of the threshold value, determines a value corresponding to the achievement of the goal. To confirm the proposed method on the array of 208269 deaths, the authors built a mathematical model using algebraic model of constructive logic. Evaluation of a mathematical model confirmed the performance of the proposed method of calculating the target value, since the simulation results are most consistent with known estimates obtained by other methods.

Improvement of Methods for the Study of Population Health Using Algebraic Models of Constructive Logic

2015 ◽  
Vol 22 (3) ◽  
pp. 8-14 ◽  
Author(s):  
Хромушин ◽  
Viktor Khromushin ◽  
Китанина ◽  
K. Kitanina ◽  
Аверьянова ◽  
...  
Keyword(s):  
Expert Systems ◽  
Mathematical Models ◽  
Population Health ◽  
Algebraic Model ◽  
Compact Representation ◽  
Constructive Logic ◽  
Algebraic Models ◽  
Source Data ◽  
Support Of Research ◽  
The Mathematical Model

Algebraic model of constructive logic is developed in Russia and is used for many years in medicine and biology for multivariate analysis and for building expert systems. In the process of improving the algorithm of the algebraic model of constructive logic and software, the methods of the study of population health with the use of these models are improved. The tasks of providing a compact representation of the mathematical model are solved, the version of algorithms and programs with different reaction to incomplete source data is created, an analytical and methodological support of research is developed. The article presents the results of practical work to improve the working methods of the study of population health. It covers the issues of verification of source data, an obtainment a compact mathematical models, the valuation and completeness of the source data, the main highlight of the resulting components, the exclusion of inconsistencies in the source data, the absorption of the analyzed factors, the principles of the analysis of the factors in mathematical models and principles of construction of expert systems. The authors showed that the classical and modernized versions of the algebraic model of constructive logic have their applications and are not exclusive of each other. This article also provides recommendations and explanations that facilitate the realization of analytical studies using algebraic models of constructive logic.

Balancing of parallel U-shaped assembly lines with a heuristic algorithm based on bidirectional priority values

2021 ◽  
pp. 1063293X2110655
Author(s):  
Yuling Jiao ◽  
Xue Deng ◽  
Mingjuan Li ◽  
Xiaocui Xing ◽  
Binjie Xu
Keyword(s):  
Mathematical Model ◽  
Heuristic Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Assembly Lines ◽  
Line System ◽  
Assembly Line Balancing Problem ◽  
The Mathematical Model ◽  
Better Than

Aiming at improving assembly line efficiency and flexibility, a balancing method of parallel U-shaped assembly line system is proposed. Based on the improved product priority diagram, the bidirectional priority value formula is obtained. Then, assembly lines are partitioned into z-q partitions and workstations are defined. After that, the mathematical model of the parallel U-shaped assembly line balancing problem is established. A heuristic algorithm based on bidirectional priority values is used to solve explanatory examples and test examples. It can be seen from the results and the effect indicators of the assembly line balancing problem that the heuristic algorithm is suitable for large balancing problems. The proposed method has higher calculation accuracy and shorter calculation time. The balancing effect of the parallel U-shaped assembly line is better than that of single U-shaped assembly line, which verifies the superiority of the parallel U-type assembly line and effectiveness of the proposed method. It provides a theoretical and practical reference for parallel U-type assembly line balancing problem.

Data preparation for multivariante analysis in medecine and biology by algebraic model of constructive logic

2016 ◽  
Vol 10 (1) ◽  
pp. 0-0
Author(s):  
Хадарцев ◽  
Aleksandr Khadartsev ◽  
Китанина ◽  
K. Kitanina ◽  
Хромушин ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Medical Information ◽  
Algebraic Model ◽  
Optimal Choice ◽  
Continuous Variables ◽  
Data Preparation ◽  
Constructive Logic ◽  
Common Mistake ◽  
Analytical Studies ◽  
Intelligent Tools

An algebraic model of constructive logic is intended to build multivariate non-linear logical mathematical model and is widely used in analytical studies in medicine and biology. An important stage of its construction is the data preparation in accordance with the following requirements: ▪ The optimal choice of the analyzed factors with the following features: the pursuit of the researcher to involve the largest possible number of factors is a common mistake; factors must be independent; it is advisable to select only those factors that meet the research objectives; the ability of the algorithm to exclude certain fac-tors from the resulting mathematical model, it is observed, when the array of data input is sufficiently complete; the ability to use both continuous variables and discrete; the mathematical model is a more compact, if less factors are used, this model is easier to analyze and identify the characteristics of the test process. ▪ Verification of the data is the identification and correction of errors at the stage of entering the informa-tion into the database; using the built-in intelligent tools that facilitate the process of correct coding of medical information; additional data verification by using special techniques, such as the analytical testing method in the database records. ▪ Selecting of objective study with the following features: the goal can be represented not only two dis-crete (the goal is achieved or not achieved), but also a large number of them; the more discrete, the amount of base should be larger; possibility to use calculated target value by using various criteria. ▪ Presence of the necessary number of entries for a full analysis, it is necessary to ensure compliance with each of the target case (corresponding to the goal) at least two cases of non-target (not corresponding to the goal). ▪ Choice of software options is adequate available data. These requirements will allow to performing the analytical studies in accordance with the required relia-bility.

An accurate and efficient control over the present numerical model of depth of sputtering in focused ion beam milling

2009 ◽  
Vol 223 (1) ◽  
pp. 9-18 ◽  
Author(s):  
S N Bhavsar ◽  
S Aravindan ◽  
P Venkateswara Rao
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Substrate Material ◽  
Milling Process ◽  
Intensity Function ◽  
Two Parameters ◽  
The Mathematical Model ◽  
Better Than

In many applications, such as fabrication of microtools, microsurgical instruments, microgears, and so on, material must be removed precisely with a focused ion beam (FIB) milling process to generate a specified geometry on substrate material. A mathematical model is available to calculate depth of sputtering at each point on substrate material in order to generate a specified geometry, but the results of the existing model deviates from experimental data. In the current paper, normalized pixel spacing and ratio of redeposition to beam velocity are the two parameters that have been considered in calculation of depth of sputtering during the FIB milling process. A proposed mathematical model incorporating the effect of redeposition has been simulated for parabolic and rectangular trench profiles, and it has been proven to be better than the existing model through comparison with experimental data of parabolic and rectangular geometry on silicon material. In addition, efforts have been made to reduce the amount of numerical calculation in the simulation process by utilizing a Gaussian mask in the existing model instead of the usual Gaussian intensity function. The Gaussian mask prevents the need for repeated calculation of Gaussian intensity function in the mathematical model of depth of sputtering, and in turn reduces the time of computation.

scholarly journals New Method of Path Optimization for Medical Logistics Robots

2021 ◽  
Vol 33 (4) ◽  
pp. 944-954
Author(s):  
Hui Jin ◽  
Qingsong He ◽  
Miao He ◽  
Fangchao Hu ◽  
Shiqing Lu ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Model Simulation ◽  
Optimization Procedure ◽  
Planning Problem ◽  
Time Cost ◽  
Problem Model ◽  
Acceleration And Deceleration ◽  
The Mathematical Model ◽  
Path Planning Problem ◽  
Better Than

The path planning problem of logistics robots is mainly subjected to the time cost of the operation of the mathematical model. To save the time of refilling process in the fast medicine dispensing system (FMDS), the optimization procedure is divided into two steps in this study. First, a new mathematical model called the multiple steps traveling salesman problem model (MTSPM) is proposed to optimize the replenishment quantity of each picking and establish picking sets. Second, an improved ant colony optimization (IACO) algorithm is employed, considering the effects of velocity, acceleration, and deceleration in the refilling route during the development of the new model. Simulation results and operational results demonstrated that MTSPM-IACO was better than both the order picking model (OPM) and MTSPM-ACO in terms of saving refilling time. Compared to the OPM, the optimization of the refilling time of MTSPM-IACO was more than 1.73% in simulation and 15.26% in operation. Compared to MTSPM-ACO, the optimization of the refilling time of MTSPM-IACO was more than 0.13% in simulation and 1.67% in operation.

Sign in / Sign up

Export Citation Format

Share Document