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Abstract Modelling of received ultrasonic signals can provide basis for both signal analysis and process in various instrumentations based on ultrasonic measurement. At present, some empirical models where the resonance frequencies set to fixed values have been proposed for this purpose. However, significant errors can be found between the existing models and actual ultrasonic signals. This paper proposes a modelling method of received ultrasonic signal based on variable frequency analysis, which is then validated by application on ultrasonic gas flowmeter. In this method, the traditional exponential model is modified to contain a time-varying frequency parameter, whose value can be estimated by analyzing the sampled signal using the Teager Energy Operator (TEO). Other parameters in the model can be further determined by parameter estimation algorithms. In order to meet TEO's application conditions, a sinusoidal transformation of the received signal using Hilbert transform before the time-varying frequency estimation is proposed. Wavelet packet de-noising method, lag parameter and smoothing spline are introduced to improve the performance of the time-varying frequency acquisition in the presence of noise. A number of numerical tests are conducted on simulated signals with different time-varying frequencies and noise to confirm the effectiveness of the proposed method. The actual application significance is validated by analysis of the actual received signals on ultrasonic gas flowmeter. Highlights • Modelling ultrasonic signal with time-varying frequency. • Time-varying frequency estimated by Teager Energy Operator. • Sinusoidal transformation of signal to improve frequency estimation precision. • Possible zero-drift compensation and standard wave gain applications. [ABSTRACT FROM AUTHOR]

Accurate determination of time-of-flight (TOF) is crucially important for precise ultrasonic flow measurement. Detection of ultrasonic signal onset (USO) is considered as an effective approach to determine the actual value of TOF. The USO can be estimated by signal fitting methods. However, the estimation accuracy and reliability of existing methods still need to be improved. This paper proposes a signal fitting method based on artificial fish swarm algorithm and particle swarm optimization combined algorithm (AFSA-PSO). In the method, AFSA is introduced to search all possible solution spaces firstly, considering the multi-modal characteristic of the objective function in signal fitting which is easily being amplified by the strong noise. Then, a feasible solution extraction strategy is proposed to extract the local optimal solution in every space. Finally, PSO is employed to further process the local solutions to obtain the accurate USO. The method is validated by both numerical and experiment tests, using simulated signals with different strength noise and measured signal in actual ultrasonic flowmeter respectively. Comparisons with the methods proposed by other researchers are also given in the paper. The proposed AFSA-PSO is found to be more accurate, more robust, having better anti-noise ability and less time-consuming under a given accuracy requirement. [ABSTRACT FROM AUTHOR]

Over the past decade, immersion lithography has been the primary technology for exposure process in semiconductor manufacturing. Compared with traditional dry lithography method, this technology improves the exposure resolution greatly by inserting a high index liquid into the gap between lens and wafer surface. Keeping the immersion liquid pure and uniform as well as avoiding residual droplets during high speed scanning motion are two challenges faced by the development of immersion lithography. Contaminations, particles, bubbles, heating and stress in the liquid will destroy the continuity of the refractive index. High speed motion of the wafer during scanning may break the meniscus stability on the interface between the liquid and the gas surrounding it, and then generate residual droplets on the wafer. All above phenomena will affect the exposure performance of immersion lithography and corresponding flow behavior control methods are required to solve the problems. This paper gives a review of the studies on flow behavior control in immersion lithography, which contains two parts: studies for liquid purity and uniformity and studies for meniscus stability. In each part, the mechanism and character of the flow behaviors as well as their effects to exposure performance are presented firstly. Then, control approaches adopted by now, including material and surface modifications as well as immersion head utilization, are introduced. In addition, the challenges faced in future studies are also pointed out. The purpose of this review paper is to help the researchers to understand the flow control problems in immersion lithography and to provide better control approaches for exposure performance improvement. [ABSTRACT FROM AUTHOR]

Accuracy of flow rate determination is very critical to an ultrasonic gas flowmeter, which is sensitive to the flow profile under measurement and cannot be obviously improved with traditional weighted integration methods for multipath transducers. Therefore, on one hand, more attentions are paid to intelligent learning algorithms (e.g. artificial neural networks) in recent studies to accurately construct the mapping relation between multipath velocities and the flow rate. However, the bottleneck that a trained network is only customized for a certain flow profile greatly restricts its application. On the other hand, many researchers turn to reconstruct the flow field but so far all flow visualization methods cost too much and are difficult to realize online. In this paper, an intelligent method of flow profile identification for multipath ultrasonic flowmeters is developed to solve the above predicament. Based on support vector machine, a multiclass identifier is constructed to automatically identify the flow profile to be measured from 65 typical flow patterns. Extremely high identification accuracy of 99.49% for test and 100% for training is achieved while the test result still reaches 88.46% even when a measurement uncertainty of ±2% is considered. A comparison with extreme leaning machine further reveals the identification performance and robustness of the proposed flow profile identifier. Different piping configurations, installation positions and angles can be therefore accurately identified, which indirectly realizes the flow visualization and can comprise an intelligent system of ultrasonic flow measurement when combined with the intelligent flow rate determination algorithms. [ABSTRACT FROM AUTHOR]

To reduce the large integration errors brought by traditional methods in gas flow measurement under complex flow field, artificial neural network, support vector machine and other intelligent algorithms have been put into use. But these intelligent methods consume much time for training and need intensive user intervention for network design. This paper proposes to apply extreme learning machine to multipath ultrasonic flowmeters, which can analytically determine the output weights of networks instead of error backpropagation algorithm and iterative tuning of the parameters, and therefore provide high metering accuracy at extremely fast learning speed as well as require least human intervention. To test its effectiveness under different flow field and sensitivity to complex flow profiles, extreme learning machine is applied to determine the flow rate under two piping configurations, which can produce mild and severe flow disturbances. The determination errors are compared with a traditional integration method on the position of 5D and 10D as well as with the path orientation of 0° and 90°. Then 7 installation angles and 9 installation positions are respectively configured to study the performance and sensitivity of UFMs to installation effects. Finally, a comparison between extreme learning machine and other two intelligent algorithms is made in training and test time, the mean squared error and the maximal metering error under severe flow disturbance. It is found that extreme learning machine has rather high determination accuracy for flow rate at extremely fast learning speed and it is insensitive to the installation effects of ultrasonic gas flowmeter. [ABSTRACT FROM AUTHOR]