CGAN-ECT: Tomography Image Reconstruction from Electrical Capacitance Measurements Using CGANs

Due to the rapid growth of Electrical Capacitance Tomography (ECT) applications in several industrial fields, there is a crucial need for developing high quality, yet fast, methodologies of image reconstruction from raw capacitance measurements. Deep learning, as an effective non-linear mapping tool for complicated functions, has been going viral in many fields including electrical tomography. In this paper, we propose a Conditional Generative Adversarial Network (CGAN) model for reconstructing ECT images from capacitance measurements. The initial image of the CGAN model is constructed from the capacitance measurement. To our knowledge, this is the first time to represent the capacitance measurements in an image form. We have created a new massive ECT dataset of 320K synthetic image measurements pairs for training, and testing the proposed model. The feasibility and generalization ability of the proposed CGAN-ECT model are evaluated using testing dataset, contaminated data and flow patterns that are not exposed to the model during the training phase. The evaluation results prove that the proposed CGAN-ECT model can efficiently create more accurate ECT images than traditional and other deep learning-based image reconstruction algorithms. CGAN-ECT achieved an average image correlation coefficient of more than 99.3% and an average relative image error about 0.07.Comment: 13 pages, 10 figures, 6 table

FPGA Implementation of ECT Digital System for Imaging Conductive Materials

This paper presents the hardware implementation of a stand-alone Electrical Capacitance Tomography (ECT) system employing a Field Programmable Gate Array (FPGA). The image reconstruction algorithms of the ECT system demand intensive computation and fast processing of large number of measurements. The inner product of large vectors is the core of the majority of these algorithms. Therefore, a reconfigurable segmented parallel inner product architecture for the parallel matrix multiplication is proposed. In addition, hardware-software codesign targeting FPGA System-On-Chip (SoC) is applied to achieve high performance. The development of the hardware-software codesign is carried out via commercial tools to adjust the software algorithms and parameters of the system. The ECT system is used in this work to monitor the characteristic of the molten metal in the Lost Foam Casting (LFC) process. The hardware system consists of capacitive sensors, wireless nodes and FPGA module. The experimental results reveal high stability and accuracy when building the ECT system based on the FPGA architecture. The proposed system achieves high performance in terms of speed and small design density

Model-Based Hardware-Software Codesign of ECT Digital Processing Unit

Image reconstruction algorithm and its controller constitute the main modules of the electrical capacitance tomography (ECT) system; in order to achieve the trade-off between the attainable performance and the flexibility of the image reconstruction and control design of the ECT system, hardware-software codesign of a digital processing unit (DPU) targeting FPGA system-on-chip (SoC) is presented. Design and implementation of software and hardware components of the ECT-DPU and their integration and verification based on the model-based design (MBD) paradigm are proposed. The inner-product of large vectors constitutes the core of the majority of these ECT image reconstruction algorithms. Full parallel implementation of large vector multiplication on FPGA consumes a huge number of resources and incurs long combinational path delay. The proposed MBD of the ECT-DPU tackles this problem by crafting a parametric segmented parallel inner-product architecture so as to work as the shared hardware core unit for the parallel matrix multiplication in the image reconstruction and control of the ECT system. This allowed the parameterized core unit to be configured at system-level to tackle large matrices with the segment length working as a design degree of freedom. It allows the trade-off between performance and resource usage and determines the level of computation parallelism. Using MBD with the proposed segmented architecture, the system design can be flexibly tailored to the designer specifications to fulfill the required performance while meeting the resources constraint. In the linear-back projection image reconstruction algorithm, the segmentation scheme has exhibited high resource saving of 43% and 71% for a small degradation in a frame rate of 3% and 14%, respectively

Smart Petri Nets Temperature Control Framework for Reducing Building Energy Consumption

Energy consumption is steadily increasing in the Kingdom of Saudi Arabia (KSA), which imposes continuous strains on the electrical load. Furthermore, consumption rationalization measures do not seem to improve the situation in any way. Therefore, the implementation of energy saving policies become an urgent need. This paper targets developing a smart energy-saving framework for integrating new advanced technologies and conventional Air Conditioning (AC) systems to achieve a comfortable environment, optimum energy efficiency and profitability. In this paper, a three-stage smart control framework, which allows controlling room temperature according to the user’s preferences, is implemented. The first stage is a user identification process. In the second stage, a Petri Nets (PN) model monitors users and sends their preferred temperatures to the third stage. A PID controller is implemented in the third stage to regulate room temperatures. The interconnected sensing and actuating devices in this smart environment are configured to provide users with comfort and energy saving functionality. Experimental results show the good performances and features of the proposed approach. The proposed smart framework reduces the energy consumption of the current ON/OFF controller ( 219.09 kW) by a significant amount which reaches ( 116.58 kW) by ratio about 46.79 % . Reducing energy consumption is one of these important features in addition to system reactivity and user comfort

A New Wide Frequency Band Capacitance Transducer with Application to Measuring Metal Fill Time

A novel low cost, high frequency circuit for measuring capacitance is proposed in this paper. This new capacitance measuring circuit is able to measure small coupling capacitance variations with high stray-immunity. Hence, it could be used in many potential applications such as measuring the metal fill time in the Lost Foam Casting (LFC) process and Electrical Capacitive Tomography (ECT) system. The proposed circuit is based on differential charging/discharging method using current feedback amplifier and a synchronous demodulation stage. The circuit has a wide high frequency operating range with zero phase shift; hence multiple circuits can work at different frequencies simultaneously to measure the capacitance. The non-ideal characteristic of the circuit has been analyzed and the results verified through LTSpice simulation. Results from the tests on a prototype and a simulation elucidate the practicality of the proposed circuit

Simulated Annealing with Exploratory Sensing for Global Optimization

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations

A Feature-Based Solution to Forward Problem in Electrical Capacitance Tomography of Conductive Materials

A new feature-based technique is introduced to solve the nonlinear forward problem (FP) of the electrical capacitance tomography with the target application of monitoring the metal fill profile in the lost foam casting process. The new technique is based on combining a linear solution to the FP and a correction factor (CF). The CF is estimated using an artificial neural network (ANN) trained using key features extracted from the metal distribution. The CF adjusts the linear solution of the FP to account for the nonlinear effects caused by the shielding effects of the metal. This approach shows promising results and avoids the curse of dimensionality through the use of features and not the actual metal distribution to train the ANN. The ANN is trained using nine features extracted from the metal distributions as input. The expected sensors readings are generated using ANSYS software. The performance of the ANN for the training and testing data was satisfactory, with an average root-mean-square error equal to 2.2%