Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN), polynomial perceptron network (PPN), and Legendre neural network (LeNN) were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model

Statistical Techniques and Artificial Neural Networks for Image Analysis

The main topic of this PhD thesis is image analysis. The subject was investigated from different perspectives, starting from different image types and with different goals. At the beginning x-ray hazelnuts images were analyzed. The target of this analysis was to determine whether a hazelnut was good or not. In order to do this an Artificial Neural Network was used, whose features were statistical variables. At a later stage a SVM was implemented to try to get better results. The second kind of images were still x-ray ones; they were, however, images coming from a PCB productive process. What we were asked to do was to highlight the air bubbles trapped into a solder joint (particularly those inside the thermal pads). In this case filters and morphological operations were used. The third case were ulcers photographs: the goal of the collaboration with SIF (Società Italiana di Flebologia, Italian society of phlebology) is to give doctors a way to evaluate ulcers remotely, in order to customize the treatments according to how the healing behaves. A small digression was the development of a small and cheap Arduino-based robot for an educational laboratory (Xkè?, in collaboration with prof. Angelo Raffaele Meo from DAUIN, Politecnico di Torino). This should have been the first step towards the development of an evolved robot for agricultural purposes, but the project didn’t start

Noise exposure in an opencast platinum mine in the Limpopo Province during 2006-2010

A research report submitted to the Faculty of Health Sciences; School of Public Health; University of Witwatersrand; Johannesburg in fulfilment of the requirements for the degree of Master in Public Health in the field of Occupational Hygiene Johannesburg; May 2015Background: Tasks aimed at increasing productivity in the opencast mining industry have indicated a need to use larger machinery together with improvements in technology. This has resulted in an increase in the use of mechanical products, which has been accompanied by an increase in occupational noise exposure levels. Dangerous occupational noise exposures might be more prevalent in the mining sector than in other industrial segments due to a large number of persons employed by the mining sector. However, given the scant literature on occupational noise exposure in opencast mines, we are unsure of the magnitude of the problem. Therefore, it is imperative to conduct a research study on occupational noise exposures in an Opencast Platinum Mine and to provide recommendations on the abatement of noise exposure to workers to mine management. Aim: This study aimed to determine if employees in the production area of an Opencast Platinum mine were over-exposed to noise levels above acceptable national and international exposure limits of 85dB(A) and 90dB(A) respectively during 2006-2010. Objectives: The main study objectives were to identify and assess occupations with significant risk to occupational noise exposure in an Opencast platinum mine production area during 2006-2010; to describe personnel noise exposure amongst the identified significant risk occupations in the same Opencast Platinum mine production area during 2006-2010. Finally, the study compared occupational noise exposure of identified significant risk occupations in the same Opencast Platinum mine production area with national and international exposure limits during 2006-2010. Methodology: The study employed a cross sectional retrospective record review of noise measurement data collected during a 5-year period. Statistical analyses were conducted using S-PLUS (version 8.1) and SAS System Software packages (version 9.1). To describe the measures of central similarity and distribution of the noise levels, arithmetic mean (AM) median, geometric means (GMs) and geometric standard deviations were presented in tables. Results: During the hazard identification process ten occupations were identified as significant noise risk exposed occupations, the shovel operator was the lowest exposed occupation with a minimum noise level measurement of 78.40dB (A) (TWA.8h) and maximum-noise level of 96.95dB (A) (TWA.8h). The drill rig operator was one of the top 3 most exposed occupations with a 90th percentile of 98.13dB (A) (TWA.8h). The drill foreman with a maximum of 99.75 dB (A) and a 90th percentile of 96.93dB (A) (TWA.8h) exceed the South African Department of Minerals and Resources (DMR) OEL of 85dB (A) (TWA.8h). From the total amount of three thousand one hundred and sixty (3160), ninety eight percent (98.92%) of the measured time weighted 8 Noise Exposure in an Opencast Platinum Mine in theLimpopo Province during 2006 – 2010 hours average (TWA.8h) results exceed the South African Department of Minerals and Resources (DMR) OEL of 85dB (A) , 65% exceeded the Occupational Health and Safety Administration (OHSA) PEL of 90dB(A) for noise. The front-end loader operator had the highest percentage of measurements (81.65%) exceeding the Occupational Health and Safety Administration (OHSA) PEL of 90dB (A) for noise exposure in the time frame 2006-2010. Conclusions: This study showed that there is substantial risk for overexposure to noise in occupations working in the production area of an opencast mine. Task type and duration associated with production in the opencast mine may determine whether employees are exposed to noise > 85dB (A) (TWA.8h). Hence equipment type, maintenance of controls and employee risk reduction behaviour may be important elements of noise exposure. Identifying noise exposure elements and contributing sources will be of value when improving or implementing a new control at the noise source. Development of methodical and comprehensive hearing conservation programme for lowering the noise level in workplaces and prevention of occupational noise induced hearing loss, at the place of work is suggested

Implementation of ANN in Software Effort Estimation: Boundary Value Effort Forecast: A novel Artificial Neural Networks model to improve the accuracy of Effort Estimation in Software Development Projects

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementSoftware Development consistently accommodates a variety of unstable scenarios. Good planning always stands behind well-defined requirements. Hence, the consistency of the effort estimation plays a special role in the traditional Business-Consumer relationship. While the proposed models may provide high accuracy in predicting specific data sets, it’s still difficult for IT specialists/organizations to find the best method for evaluating certain functionalities. The challenge of the project; initiated programming language, project infrastructure, and/or staff experimentation are just a few of the reasons that lead to inequality in these terms. Conceptually, the planned work going to explicate the main correlations. It will contain historical background - as to how was the industrial lifecycle before pre-processing progress/what was the necessity for them to exist, as well as modern usage area of BPM and Project Management – like how managers and owners’ moves are intending to keep the consumer’s satisfaction in higher level while increasing the revenue. Taking the most failure causes of projects into consideration, the research will capture some components of Software Project Management to clarify developed approaches and their advantages and/or disadvantages. The study may also lead somehow to the Business Process Management to see the alignments of required tasks in a rigorous way. The research is generally intending to define the key features of the Project Effort Estimation as usage of the datasets, evaluating the architectures, etc. The investigation also aims to find effective causes of poor effort estimation and analyze how those improvable points may be developed to ensure a highly accurate Artificial Neural Networks model

Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN), polynomial perceptron network (PPN), and Legendre neural network (LeNN) were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model

Individual and ensemble functional link neural networks for data classification

This study investigated the Functional Link Neural Network (FLNN) for solving data classification problems. FLNN based models were developed using evolutionary methods as well as ensemble methods. The outcomes of the experiments covering benchmark classification problems, positively demonstrated the efficacy of the proposed models for undertaking data classification problems

Interval and Fuzzy Computing in Neural Network for System Identification Problems

Increase of population and growing of societal and commercial activities with limited land available in a modern city leads to construction up of tall/high-rise buildings. As such, it is important to investigate about the health of the structure after the occurrence of manmade or natural disasters such as earthquakes etc. A direct mathematical expression for parametric study or system identification of these structures is not always possible. Actually System Identification (SI) problems are inverse vibration problems consisting of coupled linear or non-linear differential equations that depend upon the physics of the system. It is also not always possible to get the solutions for these problems by classical methods. Few researchers have used different methods to solve the above mentioned problems. But difficulties are faced very often while finding solution to these problems because inverse problem generally gives non-unique parameter estimates. To overcome these difficulties alternate soft computing techniques such as Artificial Neural Networks (ANNs) are being used by various researchers to handle the above SI problems. It is worth mentioning that traditional neural network methods have inherent advantage because it can model the experimental data (input and output) where good mathematical model is not available. Moreover, inverse problems have been solved by other researchers for deterministic cases only. But while performing experiments it is always not possible to get the data exactly in crisp form. There may be some errors that are due to involvement of human or experiment. Accordingly, those data may actually be in uncertain form and corresponding methodologies need to be developed. It is an important issue about dealing with variables, parameters or data with uncertain value. There are three classes of uncertain models, which are probabilistic, fuzzy and interval. Recently, fuzzy theory and interval analysis are becoming powerful tools for many applications in recent decades. It is known that interval and fuzzy computations are themselves very complex to handle. Having these in mind one has to develop efficient computational models and algorithms very carefully to handle these uncertain problems. As said above, in general we may not obtain the corresponding input and output values (experimental) exactly or in crisp form but we may have only uncertain information of the data. Hence, investigations are needed to handle the SI problems where data is available in uncertain form. Identification methods with crisp (exact) data are known and traditional neural network methods have already been used by various researchers. But when the data are in uncertain form then traditional ANN may not be applied. Accordingly, new ANN models need to be developed which may solve the targeted uncertain SI problems. Hence present investigation targets to develop powerful methods of neural network based on interval and fuzzy theory for the analysis and simulation with respect to the uncertain system identification problems. In this thesis, these uncertain data are assumed as interval and fuzzy numbers. Accordingly, identification methodologies are developed for multistorey shear buildings by proposing new models of Interval Neural Network (INN) and Fuzzy Neural Network (FNN) models which can handle interval and fuzzified data respectively. It may however be noted that the developed methodology not only be important for the mentioned problems but those may very well be used in other application problems too. Few SI problems have been solved in the present thesis using INN and FNN model which are briefly described below. From initial design parameters (namely stiffness and mass in terms of interval and fuzzy) corresponding design frequencies may be obtained for a given structural problem viz. for a multistorey shear structure. The uncertain (interval/fuzzy) frequencies may then be used to estimate the present structural parameter values by the proposed INN and FNN. Next, the identification has been done using vibration response of the structure subject to ambient vibration with interval/fuzzy initial conditions. Forced vibration with horizontal displacement in interval/fuzzified form has also been used to investigate the identification problem. Moreover this study involves SI problems of structures (viz. shear buildings) with respect to earthquake data in order to know the health of a structure. It is well known that earthquake data are both positive and negative. The Interval Neural Network and Fuzzy Neural Network model may not handle the data with negative sign due to the complexity in interval and fuzzy computation. As regards, a novel transformation method have been developed to compute response of a structural system by training the model for Indian earthquakes at Chamoli and Uttarkashi using uncertain (interval/fuzzified) ground motion data. The simulation may give an idea about the safety of the structural system in case of future earthquakes. Further a single layer interval and fuzzy neural network based strategy has been proposed for simultaneous identification of the mass, stiffness and damping of uncertain multi-storey shear buildings using series/cluster of neural networks. It is known that training in MNN and also in INN and FNN are time consuming because these models depend upon the number of nodes in the hidden layer and convergence of the weights during training. As such, single layer Functional Link Neural Network (FLNN) with multi-input and multi-output model has also been proposed to solve the system identification problems for the first time. It is worth mentioning that, single input single output FLNN had been proposed by previous authors. In FLNN, the hidden layer is replaced by a functional expansion block for enhancement of the input patterns using orthogonal polynomials such as Chebyshev, Legendre and Hermite, etc. The computations become more efficient than the traditional or classical multi-layer neural network due to the absence of hidden layer. FLNN has also been used for structural response prediction of multistorey shear buildings subject to earthquake ground motion. It is seen that FLNN can very well predict the structural response of different floors of multi-storey shear building subject to earthquake data. Comparison of results among Multi layer Neural Network (MNN), Chebyshev Neural Network (ChNN), Legendre Neural Network (LeNN), Hermite Neural Network (HNN) and desired are considered and it is found that Functional Link Neural Network models are more effective and takes less computation time than MNN. In order to show the reliability, efficacy and powerfulness of INN, FNN and FLNN models variety of problems have been solved here. Finally FLNN is also extended to interval based FLNN which is again proposed for the first time to the best of our knowledge. This model is implemented to estimate the uncertain stiffness parameters of a multi-storey shear building. The parameters are identified here using uncertain response of the structure subject to ambient and forced vibration with interval initial condition and horizontal displacement also in interval form