Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed

Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition

Handwritten digits recognition has been treated as a multi-class classification problem in the machine learning context, where each of the ten digits (0-9) is viewed as a class and the machine learning task is essentially to train a classifier that can effectively discriminate the ten classes. In practice, it is very usual that the performance of a single classifier trained by using a standard learning algorithm is varied on different data sets, which indicates that the same learning algorithm may train strong classifiers on some data sets but weak classifiers may be trained on other data sets. It is also possible that the same classifier shows different performance on different test sets, especially when considering the case that image instances can be highly diverse due to the different handwriting styles of different people on the same digits. In order to address the above issue, development of ensemble learning approaches have been very necessary to improve the overall performance and make the performance more stable on different data sets. In this paper, we propose a framework that involves CNN based feature extraction from the MINST data set and algebraic fusion of multiple classifiers trained on different feature sets, which are prepared through feature selection applied to the original feature set extracted using CNN. The experimental results show that the classifiers fusion can achieve the classification accuracy of ≥ 98%

Bacteria classification with an electronic nose employing artificial neural networks

This PhD thesis describes research for a medical application of electronic nose technology. There is a need at present for early detection of bacterial infection in order to improve treatment. At present, the clinical methods used to detect and classify bacteria types (usually using samples of infected matter taken from patients) can take up to two or three days. Many experienced medical staff, who treat bacterial infections, are able to recognise some types of bacteria from their odours. Identification of pathogens (i.e. bacteria responsible for disease) from their odours using an electronic nose could provide a rapid measurement and therefore early treatment. This research project used existing sensor technology in the form of an electronic nose in conjunction with data pre-processing and classification methods to classify up to four bacteria types from their odours. Research was performed mostly in the area of signal conditioning, data pre-processing and classification. A major area of interest was the use of artificial neural networks classifiers. There were three main objectives. First, to classify successfully a small range of bacteria types. Second, to identify issues relating to bacteria odour that affect the ability of an artificially intelligent system to classify bacteria from odour alone. And third, to establish optimal signal conditioning, data pre-processing and classification methods. The Electronic Nose consisted of a gas sensor array with temperature and humidity sensors, signal conditioning circuits, and gas flow apparatus. The bacteria odour was analysed using an automated sampling system, which used computer software to direct gas flow through one of several vessels (which were used to contain the odour samples, into the Electronic Nose. The electrical resistance of the odour sensors were monitored and output as electronic signals to a computer. The purpose of the automated sampling system was to improve repeatability and reduce human error. Further improvement of the Electronic Nose were implemented as a temperature control system which controlled the ambient gas temperature, and a new gas sensor chamber which incorporated improved gas flow. The odour data were collected and stored as numerical values within data files in the computer system. Once the data were stored in a non-volatile manner various classification experiments were performed. Comparisons were made and conclusions were drawn from the performance of various data pre-processing and classification methods. Classification methods employed included artificial neural networks, discriminant function analysis and multi-variate linear regression. For classifying one from four types, the best accuracy achieved was 92.78%. This was achieved using a growth phase compensated multiple layer perceptron. For identifying a single bacteria type from a mixture of two different types, the best accuracy was 96.30%. This was achieved using a standard multiple layer perceptron. Classification of bacteria odours is a typical `real world' application of the kind that electronic noses will have to be applied to if this technology is to be successful. The methods and principles researched here are one step towards the goal of introducing artificially intelligent sensor systems into everyday use. The results are promising and showed that it is feasible to used Electronic Nose technology in this application and that with further development useful products could be developed. The conclusion from this thesis is that an electronic nose can detect and classify different types of bacteria

Air pollution forecasts: An overview

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies

Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist\u27s sensitivity 75%

Automotive climate control based on thermal state estimation

Abstract available: p.ii