Identification of inorganic salts and determination of their concentrations in aqueous solutions based on the valence Raman band of water using artificial neural networks

The characteristic features of the valence Raman band of water in the solutions of electrolytes are revealed. These features allow the noncontact recognition of the type of salt and the determination of its concentration in aqueous solutions using artificial neural networks. Viktor V. Fadeev. Born 1935. Graduated from the Physics Faculty of Moscow State University in 1959. Received candidate’s degree in 1967 and doctoral degree in 1983. Professor of the Physics Faculty, Moscow State University. Scientific interests: optics, laser physics, spectroscopy, and inverse problems. Author of more than 300 papers and a discovery diploma. Laureate of the USSR State Prize (1983). Tat’yana A. Dolenko. Born 1961. Graduated from the Physics Faculty of Moscow State University in 1983. Received candidate’s degree in 1987. Senior Researcher of the Physics Faculty, Moscow State University. Scientific interests: laser spectroscopy, Raman spectroscopy of aqueous media, inverse problems, and artificial neural networks. Author of 57 papers and an invention certificate. Laureate of the Lenin Komsomol Prize (1985). Sergei A. Burikov. Born 1978. Graduated from the Physics Faculty of Moscow State University in 2002. Junior Researcher of the Physics Faculty, Moscow State University. Scientific interests: optics, Raman spectroscopy of aqueous media, inverse problems, and artificial neural networks. Author of 14 papers. Aleksandr V. Sugonyaev. Born 1982. Graduated from the Physics Faculty of Moscow State University in 2005. Scientific interests: Raman spectroscopy of aqueous media, inverse problems, and artificial neural networks. Author of 5 papers.     

Use of probabilistic neural networks to solve classification problems in surgery

The paper considers the classification of peritonitis-stricken patients with regard to the outcome of the operation and the probable result of the treatment. A probabilistic neural network is offered as a classifier.     

Application of artificial neural networks for water quality prediction

The term “water quality” is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

     

Application of self-organizing maps and multilayer neural networks in problems of person identification

The problem of identifying individualizing characteristics of an object using information on bone chemistry extracted from data arrays as a base is considered. It is shown that the use of self-organizing maps allows one to detect the presence of structuredness in learning data arrays and to discriminate significant inputs. Multilayer neural networks are used to reproduce the dependences discovered. Both of the computational tools are considered to be components of the software system of person identification. Igor’ Evgen’evich Shepelev. Born in 1977. Graduated from Rostov State University in 2000 and received his candidate’s degree in 2004. He is currently is with the Research Institute of Neurocybernetics of the South Federal University as a senior research fellow. Scientific interests include artificial neural networks for problems of pattern recognition and robot technology. He is the author of 19 publications.     

用启发算法和神经网络法解决二维不规则零件排样问题

本文提出一种用启发算法和神经网络法相结合的算法解决二维不规则零件的排料问题。此算法具有优化效果好、自动化程度高、并且速度快等特点。     

The use of direct inverse maps to solve material identification problems: pitfalls and solutions

Material parameter identification is a technique that is used to calibrate material models, often a precursor to perform an industrial analysis. Conventional material parameter identification methods estimate the material parameters for a material model by solving an optimisation problem. An alternative but lesser-known approach, called a direct inverse map, directly maps the measured response to the parameters of a material model. In this study we investigate the potential pitfalls of the well-known stochastic noise and lesser-known model errors when constructing direct inverse maps. We show how to address these problems, explaining in particular the importance of projecting the measured response onto the domain of the simulated responses before mapping it to the material parameters. This paper concludes by proposing partial least squares regression as an elegant and computationally efficient approach to address stochastic and systematic (model) errors. This paper also gives insight into the nature of the inverse problem under consideration.     

Application of multi-layer perceptron neural networks to vision problems

This paper discusses the application of a class of feed-forward Artificial Neural Networks (ANNs) known as Multi-Layer Perceptrons(MLPs) to two vision problems: recognition and pose estimation of 3D objects from a single 2D perspective view; and handwritten digit recognition. In both cases, a multi-MLP classification scheme is developed that combines the decisions of several classifiers. These classifiers operate on the same feature set for the 3D recognition problem whereas different feature types are used for the handwritten digit recognition. The backpropagationlearning rule is used to train the MLPs. Application of the MLP architecture to other vision problems is also briefly discussed.     

The quantification of pollutants in drinking water by use of artificial neural networks

Drinking water attained from aquifers (ground water) is susceptible to contamination from a wide variety of sources. The importance of ensuring that the water is of high quality is paramount. Multivariate calibration in conjunction with analytical techniques can assist in qualifying and quantifying a wide range of pollutants. These can be divided into two types: inorganic and organic. The former typically includes heavy metals such as cadmium and lead; the latter includes a range of compounds such as pesticides and by-products of industrial processes such as oil refining. This article presents the application of the well known nature-inspired paradigm of artificial neural networks (ANNs) for the quantitative determination of inorganic pollutants (namely cadmium, lead and copper) and organic pollutants (namely anthracene, phenanthrene and naphthalene) from multivariate analytical data acquired from the samples. The success of the determination of the pollutants via ANNs is reported in terms of the overall root mean square error of prediction (RMSEP) which is an accepted measure of the difference between the predicted concentrations and the actual concentrations. The work represents a good example of nature-inspired methods being used to solve a genuine environmental problem.     

A new result for projection neural networks to solve linear variational inequalities and related optimization problems

In recent years, a projection neural network was proposed for solving linear variational inequality (LVI) problems and related optimization problems, which required the monotonicity of LVI to guarantee its convergence to the optimal solution. In this paper, we present a new result on the global exponential convergence of the projection neural network. Unlike existing convergence results for the projection neural network, our main result does not assume the monotonicity of LVI problems. Therefore, the projection neural network can be further guaranteed to solve a class of non-monotone LVI and non-convex optimization problems. Numerical examples illustrate the effectiveness of the obtained result.     

Retraction Note to: Prediction water absorption resistance of lightweight geopolymers by artificial neural networks

Neural Computing and Applications - The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including.     

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.     

人工神经网络在传感器数据融合中的应用

针对压力传感器对温度的交叉灵敏度，采用BP人工神经网络法对其进行数据融合处理。消除温度对压力传感器的影响，大大提高了传感器的稳定性及其精度，效果良好。     

人工神经网络阻抑动力学光度法同时测定邻苯二酚和间苯二酚

研究发现:在pH=8的NaH2PO4-Na2HPO4的缓冲溶液中,邻苯二酚和间苯二酚都能明显的阻抑H2O2氧化灿烂甲酚蓝的褪色.但二者存在一定的速率差异,反应速率常数之比随时间而变化,且吸光度的加和性不佳.结合人工神经网络提出了1种能同时测定邻苯二酚和间苯二酚异构体的新方法.该方法无需预先分离或加入掩蔽剂等其他物质,最大限度保护样品不被破坏而较容易地直接进行有机物同分异构体的同时测定.本文研究了最佳反应和测定条件以及神经网络参数对网络性能的影响.并将该方法用于合成样品及水样中邻苯二酚和间苯二酚的测定,结果表明,精密度和准确度均满足分析要求.     

Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks

This work deals specifically with the use of a neural network for ozone modelling in the lower atmosphere. The development of a neural network model is presented to predict the tropospheric (surface or ground) ozone concentrations as a function of meteorological conditions and various air quality parameters. The development of the model was based on the realization that the prediction of ozone from a theoretical basis (i.e. detailed atmospheric diffusion model) is difficult. In contrast, neural networks are useful for modelling because of their ability to be trained using historical data and because of their capability for modelling highly non-linear relationships. The network was trained using summer meteorological and air quality data when the ozone concentrations are the highest. The data were collected from an urban atmosphere. The site was selected to represent a typical residential area with high traffic influences. Three neural network models were developed. The main emphasis of the first model has been placed on studying the factors that control the ozone concentrations during a 24-hour period (daylight and night hours were included). The second model was developed to study the factors that regulate the ozone concentrations during daylight hours at which higher concentrations of ozone were recorded. The third model was developed to predict daily maximum ozone levels. The predictions of the models were found to be consistent with observations. A partitioning method of the connection weights of the network was used to study the relative percent contribution of each of the input variables. The contribution of meteorology on the ozone concentration variation was found to fall within the range 33.15–40.64%. It was also found that nitrogen oxide, sulfur dioxide, relative humidity, non-methane hydrocarbon and nitrogen dioxide have the most effect on the predicted ozone concentrations. In addition, temperature played an important role while solar radiation had a lower effect than expected. The results of this study indicate that the artificial neural network (ANN) is a promising method for air pollution modelling.     

Control Liapunov function design of neural networks that solve convex optimization and variational inequality problems

This paper presents two neural networks to find the optimal point in convex optimization problems and variational inequality problems, respectively. The domain of the functions that define the problems is a convex set, which is determined by convex inequality constraints and affine equality constraints. The neural networks are based on gradient descent and exact penalization and the convergence analysis is based on a control Liapunov function analysis, since the dynamical system corresponding to each neural network may be viewed as a so-called variable structure closed loop control system.     

BP人工神经网络在bug分析中的应用

应用于金融领域的软件系统,由于其包含复杂的商业逻辑导致此类系统不但庞大而且逻辑复杂。在此类系统的开发和升级过程中,系统缺陷及错误的寻找、分析常常非常困难且费时,在通常情况下,它往往成为整个项目中后期的瓶颈。运用BP人工神经网络的算法,设计并实现了针对某银行网上交易系统的缺陷及错误分析系统,并且通过实验证实该系统能帮助开发人员提高寻找、分析系统缺陷及错误的效率,进而加快整个项目的进度。     

BP网络在催化剂活性预测中的应用

为了更好的掌握炼油企业实际生产中催化剂的用量,节约能源,减少不必要的浪费,将BP神经网络应用于催化剂活性的预测上,建立预测模型,进行训练并进行检验.该BP网络预测模型给工艺操作人员提供了一个观察催化剂活性及反应再生状态变化的窗口,使操作人员可以更好的掌控生产过程,随时调整催化剂的用量,让催化裂化装置在最优的状态下实现稳定工作,提高生产效率,增强催化裂化装置的抗干扰能力.     

Evaluation of a pressure head and pressure zones in water distribution systems by artificial neural networks

Neural Computing and Applications - Water distribution system design is inherently associated with hydraulic calculations that require thorough evaluation of obtained results and accuracy of the...     

RETRACTED ARTICLE: Prediction water absorption resistance of lightweight geopolymers by artificial neural networks

In the present study, the Charpy impact energy of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled in crack divider configuration. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using respectively 174 and 120 experimental data were conducted. A good fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the Charpy impact energy of the corresponding layer. Finally, by applying the rule of mixtures, Charpy impact energy of functionally graded steels in crack divider configuration was found through numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.

     

Application of artificial neural networks in the diagnosis of urological dysfunctions

In this article, we evaluate the work out of some artificial neural network models as tools for support in the medical diagnosis of urological dysfunctions. We develop two types of unsupervised and one supervised neural network. This scheme is meant to help the urologists in obtaining a diagnosis for complex multi-variable diseases and to reduce painful and costly medical treatments since neurological dysfunctions are difficult to diagnose. The clinical study has been carried out using medical registers of patients with urological dysfunctions. The proposal is able to distinguish and classify between ill and healthy patients.