Image reconstruction technique for ultrasonic transmission tomography

Precise flow control has always been a necessity for developing easier approaches or instrumentation for two-phase flow regime. An important method for monitoring this process is called process tomography such as electrical tomography, optical tomography and ultrasonic tomography (UT). In the case of high-acoustic impedance mixtures e.g. bubbly flow, UT has the advantages in monitoring real time data. Although various researches were conducted using UT systems in bubbly flow regimes, there are still weaknesses especially in real time image reconstruction techniques for monitoring the process. Some efforts such as linear back projection (LBP), filter back projection (FBP), convolution back projection (CBP) and iterative techniques are utilized for reconstructing the image with few views data for UT system. Regardless of the utilized method there still exist two main issues in UT image reconstruction both in forward and inverse problems. In the case of forward problem, the gaps between sensitivity maps cause artifacts in a reconstructed image. Moreover, for inverse problem, limited number of sensors causes artifacts in reconstructed image. In the case of high noisy environment, the LBP, FBP and CBP methods are not capable of totally removing the noise and artifacts level. Dynamic motion of flow regime is considered as another issue in UT system which causes inaccuracy in image reconstruction. Therefore, these issues were considered in developing a modified image reconstruction algorithm which was based on improving the CBP algorithm both in forward and inverse problems. A modified sensitivity map based on Gaussian distribution was utilized to combat the gaps in forward problem, and for the case of inverse problem, the wavelet fusion technique was applied to reduce the noise level, artifacts and the effects of dynamic motions. The simulation and the experimental works had been conducted based on different static profiles. Various types of image reconstruction algorithms were implemented and compared with the proposed technique. The quality of the final reconstructed images was evaluated using structural similarity (SSIM) and peak signal to noise ratio (PSNR). Results show that the WCBP outperforms LBP and CBP in case of SSIM and PSNR. Comparing to LBP, the SSIM and PSNR were improved at least by 30% and 5% respectively while for CBP the improvement were about 5% and 1% respectively

An ultrasonic system for profiling bubblers in water

Multi-phase flow occurs as two or more discrete phases flow in a closed pipe or a vessel. Examples of phases include gas, liquid or solid and also different immiscible liquids or solids[1]. Two phase flow of fluids (e.g. gas/liquid, liquid/liquid, etc.) is an important phenomenon in which two immiscible phases coexist in a thermodynamic equilibrium. As a two phase flow regime, bubbly flow column are intensively used as multiphase contactors and reactors in chemical, biochemical and petrochemical industries. Investigation of design parameters characterizing the operation and transport phenomena of bubble columns have led to better understanding of the hydrodynamic properties, heat and mass transfer mechanisms and flow regime characteristics ongoing during the operation[2, 3]. Due to the stringent regulations on precise flow control especially in the case of two phase fluid flow,, there has always been a necessity for developing an easier to use, yet more precise approaches or instrumentation. Accordingly, tomographic measurement is more significant and attractable especially in today's industrial process .

Image reconstruction methods for ultrasonic transmission mode tomography in bubbly flow regime

Image reconstruction from projections plays an important role in monitoring flow regimes by ultrasonic transmission mode tomography (UTMT) system. Fast and more accurate methods are necessary in case of on-line process e.g. bubbly flow regimes. In this work, analytical image reconstruction methods such as linear back projection (LBP), filter back projection (FBP) and convolution back projection (CBP) in bubbly flow regime is investigated and found that CBP is superior to other methods. Furthermore, different filters were applied to CBP to investigate the image quality improvement. Among different types of filters for CBP method, Ram-lack outperforms the others for UTMT. The peak signal to noise ratio (PSNR) of reconstructed images in this particular experiment was improved using Ram-lack in noiseless data

An assessment of stingless beehive climate impact using multivariate recurrent neural networks

A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation

Medium size dual-axis solar tracking system with sunlight intensity comparison method and fuzzy logic implementation

Nowadays, renewable energy such as solar power has become important for electricity generation, and solar power systems have been installed in homes. Furthermore, solar tracking systems are being continuously improved by researchers around the world, who focus on achieving the best design and thus maximizing the efficiency of the solar power system. In this project, a fuzzy logic controller has been integrated and implemented in a medium-scale solar tracking system to achieve the best real-time orientation of a solar PV panel toward the sun. This project utilized dual-axis solar tracking with a fuzzy logic intelligent method. The hardware system consists of an Arduino UNO microcontroller as the main controller and Light Dependent Resistor (LDR) sensors for sensing the maximum incident intensity of solar irradiance. Initially, two power window motors (one for the horizontal axis and the other for the vertical axis) coordinate and alternately rotate to scan the position of the sun. Since the sun changes its position all the time, the LDR sensors detect its position at five-minute intervals through the level of incident solar irradiance intensity measured by them. The fuzzy logic controller helps the microcontroller to give the best inference concerning the direction to which the solar PV panel should rotate and the position in which it should stay. In conclusion, the solar tracking system delivers high efficiency of output power with a low power intake while it operates

Medium Optimization for Synaptobrevin Production Using Statistical Methods

Background: Botulinum toxin, the most potent biological toxin, has become a powerful therapeutic tool for a growing number of clinical applications. Molecular studies have identified a family of synaptic vesicle-associated membrane proteins (VAMPs, also known as synaptobrevins) which have been implicated in synaptic vesicle docking and fusion with plasma membrane proteins.Materials and Methods: Using the synaptobrevin as a substrate for in vitro assay is the method to detect BoNT activity. We have been working on optimizations of bacterial expression conditions and media for high-level production of synaptobrevin peptide. Statistics-based experimental design was used to investigate the effect of medium components (E. coli strain, peptone, IPTG, yeast extract, ampicillin, and temperature) on synaptobrevin production by E. coli.Results: A 24 fractional factorial design with center points revealed that IPTG and temperature were the most significant factors, whereas the other factors were not important within the levels tested. This purpose was followed by a central composite design to develop a response surface for medium optimization. The optimum medium composition for synaptobrevin production was found to be: IPTG 29 mM, peptone 10 g/L, yeast extract 5 g/L, temperature 23°C and ampicillin 100 mg/L. This medium was projected to produce, theoretically, 115 mg/Lsynaptobrevin.Conclusion: The optimum medium composition synaptobrevin production was found to be: BL21 (E.coli strain), LB medium (peptone 10 g/L, Yeast 5 g/L), Ampicillin (100 mg/L), IPTG (0.29 mg/L) and temperature (23°C)

The application of WiFi-based wireless sensor network (WSN) in hill slope condition monitoring

In this paper, a wireless sensor network for landslide monitoring (WSNLM) system is described. WSNLM utilized a wireless protocol which is 802.11g. The hardware structure of the WSNLM is discussed where the important parts had been discussed in details. In order to assess the susceptibility of a hill slope to landslide, several parameters had been considered for the network. The important factors that affect landslide is the ground status, which is soil moisture, vibration in the land and also soil temperature. Other factors that can relate to landslide is the environment of the surrounding such as air temperature, humidity and atmospheric pressure. The outputs from the ADXL335 accelerometer were used for slope angle measurement. The output of a vibration transducer was also used to monitor the hill slope. To account for the susceptibility of the hill slope to the land slide, safety factor value is calculated in real time. The outcomes show that the average moisture content in the soil is around 3% on a sunny day and the safety factor for a sunny day is around 75. The moisture content in the soil on a rainy day increases tremendously to more than 20%. At the same time, the safety factor drops to around 70. The system in this paper has the potential to be used as a useful tool for the detection of lanslides

Application of optical tomography for monitoring gas bubbles flow based on independent component analysis algorithm

This paper presents the monitoring process of gas bubbles flow in water using an optical tomography system. The system is aided by an Independent Component Analysis (ICA) algorithm for distinguishing the gas bubbles in pure water. The optical attenuation model is implemented for studying the light transmissions to different media which is water and air. Several quantities of air are inserted using an air pump which is installed at the bottom of a flow pipe in order to produce the gas bubbles flow upwards. The quantity of air is controlled by using a valve and five types of bubble flow are investigated; a single bubble flow, double bubble’s flow, 25% of air opening, 50% of air opening and 100% of air opening. The concentration profiles of the gas bubble flow are constructed. The concentration profile obtained from the experiments shows that the ICA algorithm can be used as a tool for imaging the two-phase flow phase distribution

Gravitational search algorithm optimization for PID controller tuning in waste-water treatment process

This paper presents a new approach of optimization technique in the controller parameter tuning for waste-water treatment process (WWTP) application. In the case study of WWTP, PID controller is used to control substrate (S) and dissolved oxygen (DO) concentration level. Too many parameters that need to be controlled make the system becomes complicated. Gravitational Search Algorithm (GSA) is used as the main method for PID controller tuning process. GSA is based on Newton's Law of Gravity and mass interaction. In this algorithm, the searcher agents survey the masses that interact with each other using law of gravity and law of motion. For WWTP system, the activated sludge reactor is used and this system is multi-input multi-output (MIMO) process. MATLAB is used as the platform to perform the simulation, where this optimization is compared to other established optimization method such as the Particle Swarm Optimization (PSO) to determine whether GSA has better features compared to PSO or vice-versa. Based on this case-study, the results show that transient response of GSA-PID was 20%-30% better compared to transient response of the PSO-PID controlle

Edge detection algorithm for enhancement of linear back projection tomographic images

Process tomography (PT) is a leading technique for multiphase flow measurement and flow monitoring systems in various fields. PT has the advantage of interpreting acquired measurement data and transforming it into visual tomographic images. The most common method of image reconstruction uses the linear back projection algorithm which often results in blurry images. This paper proposes an enhancement of the reconstructed images using an edge detection image processing technique to convolve with the original image. This filtering technique calculates approximation changes of the horizontal and vertical image derivatives, thus further enhancing image accuracy. Several ultrasonic tomography images were put into a simulation test to validate it. Hence, the image results are being assessed for its performanc