An improved search space resizing method for model identification by standard genetic algorithm

In this paper, a new improved search space boundary resizing method for an optimal model’s parameter identification for continuous real time transfer function by standard genetic algorithms (SGAs) is proposed and demonstrated. Premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of the search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model’s parameters for the identified transfer function. This new method is applied and examined on two processes, a third-order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method’s efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations

An improved search space resizing method for model identification by Standard Genetic Algorithm

.In this paper, a new improved search space boundary resizing method for an optimal model's parameter identification by Standard Genetic Algorithms (SGAs) is proposed and demonstrated. The premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model's parameters for the identified transfer function. This new method is applied and examined on two processes, a third order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method's efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations. © 2015 Chinese Automation and Computing Society in the UK - CAC

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Predetermined time constant approximation method for optimising search space boundary by standard genetic algorithm

In this paper, a new predetermined time constant approximation (Tsp) method for optimising the search space boundaries to improve SGAs convergence is proposed. This method is demonstrated on parameter identification of higher order models. Using the dynamic response period and desired settling time of the transfer function coefficients offered a better suggestion for initial Tsp values. Furthermore, an extension on boundaries derived from the initial Tsp values and the consecutive execution, brought the elite groups within feasible boundary regions for better exploration. This enhanced the process of locating of the optimal values of coefficients for the transfer function. The Tsp method is investigated on two processes; excess oxygen and a third order continuous model with and without random disturbance. The simulation results assured the Tsp method's effectiveness and flexibility in assisting SGAs to locate optimal transfer function coefficients. Copyright © 2015 ACM

Advanced PID Control Optimisation and System Identification for Multivariable Glass Furnace Processes by Genetic Algorithms

This thesis focuses on the development and analysis of general methods for the design of optimal discrete PID control strategies for multivariable glass furnace processes, where standard genetic algorithms (SGAs) are applied to optimise specially formulated objective functions. Furthermore, a strong emphasis is given on the realistic model parameters identi cation method, which is illustrated to be applicable to a wide range of higher order model parameters identi cation problems. A complete, realistic and continuous excess oxygen model with nonlinearity effect was developed and the model parameters were identified. The developed excess oxygen model consisted of three sub-models to characterise the real plant response. The developed excess oxygen model was evaluated and compared with real plant dynamic response data, which illustrated the high degree of accuracy of the developed model. A new technique named predetermined time constant approximation was proposed to make an assumption on the initial value of a predetermined time constant, whose motive is to facilitate the SGAs to explore and exploit an optimal value for higher order of continuous model's parameters identi cation. Also, the proposed predetermined time constant approximation technique demonstrated that the population diversity is well sustained while exploring the feasible search region and exploiting to an optimal value. In general, the proposed method improves the SGAs convergence rate towards the global optimum and illustrated the effectiveness. An automatic tuning of decentralised discrete PID controllers for multivariable processes, based on SGAs, was proposed. The main improvement of the proposed technique is the ability to enhance the control robustness and to optimise discrete PID parameters by compensating the loop interaction of a multivariable process. This is attained by adding the individually optimised objective function of glass temperature and excess oxygen processes as one objective function, to include the total effect of the loop interaction by applying step inputs on both set points, temperature and excess oxygen, at two different time periods in one simulation. The effectiveness of the proposed tuning technique was supported by a number of simulation results using two other SGAs conventional tuning techniques with 1st and 2nd order control oriented models. It was illustrated that, in all cases, the resulting discrete PID control parameters completely satisfied all performance specifications. A new technique to minimise the fuel consumption for glass furnace processes while sustaining the glass temperature is proposed. This proposed technique is achieved by reducing the excess oxygen within the optimum thermal efficiency region within 1.7% to 3.2%, which is approximately equal to about 10% to 20% of excess air. Therefore, by reducing the excess oxygen set point within the optimum region, 2.45% to 2%, the fuel consumption is minimised from 0:002942kg/sec to 0:002868kg/sec while the thermal efficiency of the glass temperature is sustained at the desired set point (1550K). In addition, a reduction in excess oxygen within methane combustion guidelines will assure that undesirable emissions are in control throughout the combustion process. The efficiencies of the proposed technique were supported by a number of simulation results applying the three SGAs controller tuning techniques. It was illustrated that, in all cases, the fraction of excess oxygen reduction results in a great minimisation of fuel consumption over long plant operating periods

TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images

Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning model based on self-attention mechanism using transformer architecture to classify the cervix images to help in diagnosis of cervical cancer. We have used techniques like an enhanced multivariate gaussian mixture model optimized with mexican axolotl algorithm for segmenting the colposcope images prior to the Temporal Lesion Convolution Neural Network (TelsNet) classifying the images. TelsNet is a transformer-based neural network that uses temporal convolutional neural networks to identify cancerous regions in colposcope images. Our experiments show that TelsNet achieved an accuracy of 92.7%, with a sensitivity of 73.4% and a specificity of 82.1%. We compared the performance of our model with various state-of-the-art methods, and our results demonstrate that TelsNet outperformed the other methods. The findings have the potential to significantly simplify the process of detecting and accurately classifying cervical cancers at an early stage, leading to improved rates of remission and better overall outcomes for patients globally

Car make and model recognition under limited lighting conditions at night

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyCar make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when licence plate numbers cannot be identified or fake number plates are used. CMMR can also be used when automatic identification of a certain model of a vehicle by camera is required. The majority of existing CMMR methods are designed to be used only in daytime when most car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. This work identifies car make and model at night by using available rear view features. A binary classifier ensemble is presented, designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and licence plates from the rear view are extracted and used in the recognition process. The majority vote of individual classifiers, support vector machine, decision tree, and k-nearest neighbours is applied to verify a target model in the classification process. The experiments on 100 car makes and models captured under limited lighting conditions at night against about 400 other car models show average high classification accuracy about 93%. The classification accuracy of the presented technique, 93%, is a bit lower than the daytime technique, as reported at 98 % tested on 21 CMMs (Zhang, 2013). However, with the limitation of car appearances at night, the classification accuracy of the car appearances gained from the technique used in this study is satisfied

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology.  By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic