Use of Soft Computing Techniques for Transducer Problems

In many control system applications Linear Variable Differential Transformer (LVDT) plays an important role to measure the displacement. The performance of the control system depends on the performance of the sensing element. It is observed that the LVDT exhibits the same nonlinear input-output characteristics. Due to such nonlinearities direct digital readout is not possible. As a result we employ the LVDTs only in the linear region of their characteristics. In other words their usable range gets restricted due to the presence of nonlinearity. If the LVDT is used for full range of its nonlinear characteristics, accuracy of measurement is severely affected. So, to reduce this nonlinearities different ANN techniques is being used such as single neuron structure, MLP structure, RBFNN and ANFIS structure. Another problem considered here is with flow measurement. Generally flow measurements uses conventional flow meters for feedback on the flow-control loop cause pressure drop in the flow and in turn lead to the usage of more energy for pumping the fluid. An alternative approach for determining the flow rate without flow meters is thought. The restriction characteristics of the flow-control valve are captured by a neural network (NN) model. The relationship between the flow rate and the physical properties of the flow as well as flow-control valve, that is, pressure drop, pressure, temperature, and flow-control valve coefficient (valve position) is found. With these accessible properties, the NN model yields the flow rate of fluid across the flow-control valve, which acts as a flow meter. The viability of the methodology proposed is illustrated by real flow measurements of water flow which is widely used in hydraulic systems. Control of fluid flow is essential in process-control plants. The signal of flow measured using the flow meter is compared with the signal of the desired flow by the controller. The controller output accordingly adjusts the opening/closing actuator of the flow-control valve in order to maintain the actual flow close to the desired flow. Typically, flow meters of comparatively low cost such as turbine-type flow meters and venturi-type meters are used to measure the volumetric quantity of fluid flow in unit time in a flow process. However, the flow i meter inevitably induces a pressure drop in the flow. In turn, this results in the use of more energy for pumping the fluid. To avoid this problem, non-contact flow meters, i.e. electromagnetic-type flow meters, have been developed and are widely used in process plants not only because there is no requirement for installation in the pipeline but also because introduction to the differential pressure across pipelines is not necessitated. Unfortunately, the cost of such non-contact measurement is comparatively much higher than that of its conventional counterparts. Here, an alternative approach is proposed to obtain the fluid flow measurement for flow-control valves without the pressure drop and the consequent power loss that appear in conventional flow meters. Without the flow meter, it is a fact that the flow rate can be determined from the characteristics of the control valve for flow measurements. In this method, the restriction characteristics of the control valve embedded in a neural network (NN) model are used in determining the flow rate instead of actual measurement using a conventional flow meter. i

Design , Development and Performance Evaluation of Intelligence Sensors

Many electronic devices, instruments and sensors exhibit inherent nonlinear input-output characteristics. Nonlinearity also creeps in due to change in environmental conditions such as temperature and humidity. In addition, aging of the sensors also introduce nonlinearity. Due to such nonlinearities direct digital readout is not possible. As a result the devices or sensors are used only in the linear region of their characteristics. In other words the usable range of these devices gets restricted due to nonlinearity problem. The accuracy of measurement is also affected if the full range of the instrument is used. The nonlinearity present in the characteristics is usually time-varying and unpredictable as it depends on many uncertain factors stated above. Hence the prime objective of the thesis is to study the nonlinearity problem associated with these devices and suggest novel methods of circumventing these effects by suitably designing intelligent systems. In the present investigation,..

Simulation and Analysis of Electro Mechanical Actuator with Position Control

In recent times, Electro-Mechanical Actuator (EMA) is widely employed in various aerospace applications because of its compactness, ease of maintenance, and cost efficiency. It attracts most of the researcher for simulation and performance analysis. It is very much important to study its control system behaviour. In general, EMA requires, three loop cascade control, but for aerospace application two loop cascade control is used for speed and position controls due to dynamic load changing requirement. Most research efforts on EMA system has used a transfer function model of all its subsystems. Nevertheless, this technique does not yield complete outcomes for analysing its performance. To analyse its performance and characteristics in dynamic condition, an experimental model is essential. In addition, this model needs to cater for analysing performance of different capacity EMA. The primary goal of this work is to simulate unique EMA model with position control using a practical data and analyse its performance. In this design, EMA is modelled by three-phase Brushless Direct Current (BLDC) motor, six-step commutation logic, a speed sensor (Tacho) and a position sensor using Linear Variable Differential Transformer (LVDT). Position and speed controls are handled by Proportional (P) and Proportional plus Integral (PI) controllers respectively. The process reaction curve method is used to tune the controllers. This tuning approach is adequate to enable accurate and robust speed and position control. This paper focus on the simulation and performance analysis of a practical EMA system with position and speed controls in matlab-simulink. The performance analysis results shows that simulated model characteristic is close to physical system and reliable

Dynamic system identification and sensor linearization using neural network techniques

Many techniques have been proposed for the identification of unknown system. The scope of the parameter approximation or estimation and system identification is growing day by day. Lots of research has been done in this field but it can be still considered as an open field for researchers. The overall field of system identification is day by day growing in the field of research and lots of methods are coming time to time. This research presents a number of results, examples and applications of parameter identification techniques. Different Methods are introduced here with less and more complexities. For System Identification some of Neural Network techniques are studied. Least mean square technique is used for the final calculations of simulation results. Simulations are done with the help of Matlab programming. Some Neural Network Techniques have been proposed here are multilayered neural Network, Functional link Layer Neural network Technique. Mainly Disadvantage of basic system identification techniques is that it use the back propagation techniques for the weight updating purpose which have a lots of computation complexity. A single layer Artificial Neural Network has been studied which is known as Functional Link Artificial Neural Network (FLANN). In such type of System Identification technique hidden layers are wipe out by functional expansion of input pattern. The prominent advantage of such type of network is that the computation complexity is much less than complexity of the multilayered neural network. In the field Control and Instrumentation there are some characteristics which are desirable for the sensors

Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches

Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT inverse problem is about inferring the internal conductivity distribution of an object from measurements taken on its boundary. It is severely ill-posed, necessitating advanced computational methods for accurate image reconstructions. Recent years have witnessed significant progress, driven by innovations in analytic-based approaches and deep learning. This review explores techniques for solving the EIT inverse problem, focusing on the interplay between contemporary deep learning-based strategies and classical analytic-based methods. Four state-of-the-art deep learning algorithms are rigorously examined, harnessing the representational capabilities of deep neural networks to reconstruct intricate conductivity distributions. In parallel, two analytic-based methods, rooted in mathematical formulations and regularisation techniques, are dissected for their strengths and limitations. These methodologies are evaluated through various numerical experiments, encompassing diverse scenarios that reflect real-world complexities. A suite of performance metrics is employed to assess the efficacy of these methods. These metrics collectively provide a nuanced understanding of the methods' ability to capture essential features and delineate complex conductivity patterns. One novel feature of the study is the incorporation of variable conductivity scenarios, introducing a level of heterogeneity that mimics textured inclusions. This departure from uniform conductivity assumptions mimics realistic scenarios where tissues or materials exhibit spatially varying electrical properties. Exploring how each method responds to such variable conductivity scenarios opens avenues for understanding their robustness and adaptability

Advective Diffusion Transformers for Topological Generalization in Graph Learning

Graph diffusion equations are intimately related to graph neural networks (GNNs) and have recently attracted attention as a principled framework for analyzing GNN dynamics, formalizing their expressive power, and justifying architectural choices. One key open questions in graph learning is the generalization capabilities of GNNs. A major limitation of current approaches hinges on the assumption that the graph topologies in the training and test sets come from the same distribution. In this paper, we make steps towards understanding the generalization of GNNs by exploring how graph diffusion equations extrapolate and generalize in the presence of varying graph topologies. We first show deficiencies in the generalization capability of existing models built upon local diffusion on graphs, stemming from the exponential sensitivity to topology variation. Our subsequent analysis reveals the promise of non-local diffusion, which advocates for feature propagation over fully-connected latent graphs, under the assumption of a specific data-generating condition. In addition to these findings, we propose a novel graph encoder backbone, Advective Diffusion Transformer (ADiT), inspired by advective graph diffusion equations that have a closed-form solution backed up with theoretical guarantees of desired generalization under topological distribution shifts. The new model, functioning as a versatile graph Transformer, demonstrates superior performance across a wide range of graph learning tasks.Comment: 39 page

Measurements, Models, Systems and Design

531 s.

Mechatronics of systems with undetermined configurations

This work is submitted for the award of a PhD by published works. It deals with some of the efforts of the author over the last ten years in the field of Mechatronics. Mechatronics is a new area invented by the Japanese in the late 1970's, it consists of a synthesis of computers and electronics to improve mechanical systems. To control any mechanical event three fundamental features must be brought together: the sensors used to observe the process, the control software, including the control algorithm used and thirdly the actuator that provides the stimulus to achieve the end result. Simulation, which plays such an important part in the Mechatronics process, is used in both in continuous and discrete forms. The author has spent some considerable time developing skills in all these areas. The author was certainly the first at Middlesex to appreciate the new developments in Mechatronics and their significance for manufacturing. The author was one of the first mechanical engineers to recognise the significance of the new transputer chip. This was applied to the LQG optimal control of a cinefilm copying process. A 300% improvement in operating speed was achieved, together with tension control. To make more efficient use of robots they have to be made both faster and cheaper. The author found extremely low natural frequencies of vibration, ranging from 3 to 25 Hz. This limits the speed of response of existing robots. The vibration data was some of the earliest available in this field, certainly in the UK. Several schemes have been devised to control the flexible robot and maintain the required precision. Actuator technology is one area where mechatronic systems have been the subject of intense development. At Middlesex we have improved on the Aexator pneumatic muscle actuator, enabling it to be used with a precision of about 2 mm. New control challenges have been undertaken now in the field of machine tool chatter and the prevention of slip. A variety of novel and traditional control algorithms have been investigated in order to find out the best approach to solve this problem

Data driven approaches for investigating molecular heterogeneity of the brain

It has been proposed that one of the clearest organizing principles for most sensory systems is the existence of parallel subcircuits and processing streams that form orderly and systematic mappings from stimulus space to neurons. Although the spatial heterogeneity of the early olfactory circuitry has long been recognized, we know comparatively little about the circuits that propagate sensory signals downstream. Investigating the potential modularity of the bulb’s intrinsic circuits proves to be a difficult task as termination patterns of converging projections, as with the bulb’s inputs, are not feasibly realized. Thus, if such circuit motifs exist, their detection essentially relies on identifying differential gene expression, or “molecular signatures,” that may demarcate functional subregions. With the arrival of comprehensive (whole genome, cellular resolution) datasets in biology and neuroscience, it is now possible for us to carry out large-scale investigations and make particular use of the densely catalogued, whole genome expression maps of the Allen Brain Atlas to carry out systematic investigations of the molecular topography of the olfactory bulb’s intrinsic circuits. To address the challenges associated with high-throughput and high-dimensional datasets, a deep learning approach will form the backbone of our informatic pipeline. In the proposed work, we test the hypothesis that the bulb’s intrinsic circuits are parceled into distinct, parallel modules that can be defined by genome-wide patterns of expression. In pursuit of this aim, our deep learning framework will facilitate the group-registration of the mitral cell layers of ~ 50,000 in-situ olfactory bulb circuits to test this hypothesis